A Review of Mid-Infrared and Near-Infrared Imaging: Principles, Concepts and Applications in Plant Tissue Analysis

New perspective: An in vitro study on inferring the post-mortem interval (PMI) of human skeletal muscle based on ATR-FTIR spectroscopy combined with machine learning

The determination of the PMI remains one of the most critical challenges within the field of forensic science. Nonetheless, the estimation of PMI has emerged as one of the most complex and challenging domains of research within the field, primarily due to the absence of precise and dependable methodologies, coupled with the advanced decomposition processes of cadavers. To address these challenges, researchers have tried many methods, such as insects, microorganisms, body fluids, and animal tissues. Nevertheless, there is a paucity of research specifically examining human skeletal muscle. In this study, we collected a substantial number of human skeletal muscle samples and employed ATR-FTIR spectroscopy combined with multiple machine learning to analyze spectral changes in skeletal muscle across different PMIs. These methods primarily included PLS-R, PCR, CLS-R, MLR, SVR, and XGB-R to predict the PMI of human skeletal muscle. The results revealed distinct spectral variations in the protein, carbohydrate, and nucleic acid regions of skeletal muscle across different PMIs. Notably, the absorption peak at the 1540 cm-1 amide II band exhibited a gradual decline over time, demonstrating significant potential for PMI estimation and decomposition tracking. Due to its operational simplicity, rapid analysis, and cost-effectiveness, ATR-FTIR spectroscopy combined with machine learning demonstrates significant practical value and forensic potential for PMI estimation.

Compositional analysis of alternative protein blends using near and mid-infrared spectroscopy coupled with conventional and machine learning algorithms

The non-invasive real-time analysis of the composition of alternative, plant-based protein sources is important to control high moisture extrusion processes and ensure the quality and texture of the final extrudates used in the elaboration of meat analogues. This study aims to analyse the composition and presence of gluten in blended plant-based alternative protein sources from pulse, cereal and pseudocereal origin by means of near infrared spectroscopy (NIRS) and mid infrared spectroscopy (MIRS) using conventional and machine learning algorithms. Blends were prepared using five alternative protein sources (barley, wheat, fava bean, lupin, and buckwheat) and spectra were acquired using a low-cost and a benchtop near-infrared spectrometer, and a mid-infrared spectrometer. Using the acquired spectra, partial least square regression (PLSR), support vector machine discriminant analysis (SVM-DA), partial least square discriminant analysis (PLS-DA), and convolutional neural networks (CNN) were used to develop predictive models to determine the composition and to identify samples containing gluten. The protein, moisture, carbohydrates and fat content in blends of alternative protein sources was determined with a RMSEP of 1.59, 0.18, 1.41, and 0.19 %, respectively, when using the benchtop NIR spectrometer and PLSR. Gluten-free samples were identified with high sensitivity (0.85) and accuracy (0.93) using PLS-DA. The study demonstrated that infrared spectroscopy can be used to analyse the composition of blends of alternative protein sources including pulses, cereals, and pseudocereals, as well as to identify gluten-free samples.

Rapid evaluation of Curcuma origin and quality based on E-eye, flash GC e-nose, and FT-NIR combined with machine learning technologies

Curcuma, a key ingredient in curry and a popular health supplement, has been subject to adulteration and fraudulent origin labeling. In this study, E-eye, Flash GC e-nose, and FT-NIR, combined with machine learning and multivariate algorithms, were employed for origin identification and quantitative prediction of curcuma constituents. The results indicated that E-eye performed poorly in origin classification, while Flash GC e-nose identified flavor markers distinguishing curcuma from different origins but lacked precise quantification. After processing the FT-NIR spectra with SNV, the accuracy of three machine learning models, including SVM, increased from 83.3 % to 100 %. Additionally, PLSR models for three constituents, including curcumin, achieved mean R2 values exceeding 0.99 in both training and prediction sets, demonstrating excellent linearity and predictive accuracy. Overall, the study demonstrated that FT-NIR combined with multivariate algorithms provides an effective and feasible method for rapid origin identification and quality assessment of curcuma.

Multiblock NIR and MIR spectralprint through AComDim to evaluate the effects of growing site, harvest season, and clone on yerba mate leaves composition

The composition of yerba mate implies significant potential in the food, pharmaceutical, and cosmetic industries, which requires standardization of the raw material. This study explores the simultaneous influence of growing sites, harvest seasons, and clones on the spectralprint of leaves through near-infrared (NIR) and mid-infrared (MIR) spectroscopy coupled with ANOVA Common Dimensions (AComDim) multivariate analysis. MIR spectroscopy identifies only the main effects of growing site and harvesting season, and the interaction between these factors. The NIR spectralprint identifies all main effects and interactions. Growing site and harvesting season individually account for approximately 7 % of the variance in the chemical composition of yerba mate, with their interaction contributing with 5.7 %. Clonal variation significantly affects the spectral profile with approximately 4 % variance, which allowed the identification of clones with the highest chemical divergence. The study demonstrates that biospectroscopics and chemometrics can enhance yerba mate quality through clonal selection and optimized agricultural practices.

ATR-FTIR spectroscopy and LDA: a rapid, non-destructive and cost effective strategy to trace the geographical origin of Cannabis sativa L

Cannabis recreational and/or medicinal use has been legalized in the past years in many states and countries. As a consequence, many Cannabis growers and product developers have emerged in a new market throughout the world; at the same time, issues regarding questionable quality control have also risen, as several reports on Cannabis users' health-related problems caused by inaccurate labeling content in Cannabis-based medicines, edibles or other derivatives are being published and brought out to the public's attention. These facts make traceability methodologies crucial whether for forensic use, such as drug trafficking eradication, or for quality control purposes of legal Cannabis and of products derived from it. Hence, the objective of this study was to analyze Cannabis by means of attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) to assess the capability of this technique to trace the geographical origin of Cannabis cultivated in Brazil and in Colorado, USA. Forty-seven samples from Brazil and 18 samples from Colorado were analyzed by ATR-FTIR. Linear discriminant analysis (LDA) was employed to source the samples. The combination of ATR-FTIR and LDA achieved up to 95.23% accuracy in assigning Cannabis samples to their geographical locations of origin in Brazil and up to 100% in Colorado.

Porcine in vitro digestion and matrix structure of undigested residue of xylanase- and cellulase-supplemented maize and wheat

BACKGROUND

This study investigated the effects of supplementing maize and wheat with a combination of xylanase and cellulase on porcine in vitro digestibility, nonstarch polysaccharides (NSP) solubilization, and matrix structure. The latter was assessed using synchrotron-based micro-computed tomography (SR-μCT) and Fourier transform mid-infrared (FTMIR) spectroscopy after porcine in vitro degradation. Cereal grains were subjected to porcine in vitro digestion with or without enzyme supplementation (20 000 U kg-1 of each enzyme) in a 2 × 2 factorial design.

RESULTS

In vitro dry matter digestibility (IVDDM) was lower for maize than for wheat (P < 0.05), and enzyme supplementation had no effect on IVDDM. Supplemental enzymes reduced arabinoxylan content in the undigested residue of wheat (53 vs 46 g kg-1, P < 0.05) but not maize (42 vs 44 g kg⁻¹; P > 0.05). Synchrotron-based micro-computed tomography imaging revealed aleurone cells with intact content in the undigested residue of both maize and wheat, regardless of enzyme supplementation. Some endosperm cells in undigested maize residue retained their content, whereas nearly all endosperm cells in undigested enzyme-unsupplemented wheat residue were empty. No endosperm cells were detected in undigested enzyme-supplemented wheat residue. Fourier transform mid-infrared imaging indicated a higher presence of phenolic compounds in maize cell walls than in wheat.

CONCLUSION

Supplemental enzymes did not affect IVDDM for wheat, as they primarily degraded endosperm cell walls, the content of which was already released by pepsin and pancreatin digestion. Similarly, IVDDM for maize remained unaffected, which was probably due to the high phenolic content of its cell walls. © 2025 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Rapid dough making quality analysis of wheat flour using Fourier transform infrared spectroscopy and chemometrics

Current methods for measuring wheat quality and dough rheology in the later stages of wheat breeding programs, including extensographs and farinographs, are costly and time-consuming. There is a significant interest in the Australian wheat industry for developing non-destructive, field-based, rapid dough-making quality assessment methods for Australian wheat varieties throughout earlier and later stages of the wheat breeding process. Fourier transform infrared (FTIR) spectroscopy is a valuable tool for analysis and quality control in the food industry as it is a simple and rapid technique requiring no sample pre-treatment before analysis. We aimed to investigate the application of FTIR spectroscopy coupled with partial least squares (PLSR) regression data analysis to rapidly assess wheat flour's dough-making quality. Results indicated that using FTIR data, PLSR could be applied to accurately predict multiple dough-making qualities, including protein content, extensibility, water absorption, dough development time (DDT), dough stability, and maximum resistance to tension (Rmax). FTIR spectroscopy could not only be used to accurately predict the dough making quality of wheat lines from an in-sample test dataset, but this method also outperformed genetic predictive analysis, an established quality-prediction method in wheat breeding, in predicting dough making quality using out-of-sample data.

Rapid classification of Camellia seed varieties and non-destructive high-throughput quantitative analysis of fatty acids based on non-targeted fingerprint spectroscopy combined with chemometrics

Camellia oil is a high-quality vegetable oil rich in unsaturated fatty acids (FAs), with quality standardization challenged by the diversity of Camellia seed varieties. This study compared spectroscopy techniques (Near-Infrared [NIR] vs Mid-Infrared [MIR] spectroscopy) and analytical models (Discriminant Analysis [DA], Partial Least Squares [PLS], and Artificial Neural Networks [ANN]), seeking to classify Camellia seed varieties and estimate oil and principal FAs composition. The PCA analysis effectively discriminated among various Camellia seed varieties, likely due to variations in their oil and principal FAs compositions. Significantly, the NIR-based DA model significantly outperformed MIR, achieving 100 % accuracy in distinguishing Camellia seed varieties. In terms of predicting the oil and principal FAs compositions in Camellia seeds, NIR-based predictions models outperformed those derived from MIR, with PLS models surpassing ANN models. This study validated the potential of NIR technology combined with chemometrics for rapid, high-throughput, non-destructive identification of Camellia seeds.

Nutritional profiling of horse gram through NIRS-based multi-trait prediction modelling

Horse gram (Macrotyloma uniflorum (Lam.) Verd.) is an underutilised legume from the Indian subcontinent. Being a nutritious legume, it plays an important role in human nutrition in developing countries like India. Conventional assessment of nutritional traits, are labour and time intensive for screening of huge germplasm, hence alternative and rapid technique for conventional method for the determination of nutritional components of horse gram flour is needed. NIRS can be used for this purpose as it gives rapid and precise results for most of the plant products. In this study, a highly diverse collection of 139 horse gram accessions was utilized to generate reference data. Prediction models were developed for protein, starch, TSS, phenols, and phytic acid using MPLS regression method with spectral preprocessing using SNV-DT to remove scatter effects and baseline noise. Models were optimized for derivatives, gap selection, and smoothening and evaluated using different statistics including RSQ, bias and RPD. The RSQ and RPD for the best fit models obtained were protein (0.701; 1.85), starch (0.987; 4.03), TSS (0.800; 4.06), phenols (0.778; 2.15) and phytic acid (0.730; 1.88) indicating developed models are good for screening large number of germplasm collections and market samples. Statistical analyses, including paired t-tests, correlation, and reliability assessments, validated the strength of these models. This study represents the first report introducing a rapid, multi-trait evaluation approach for horse gram germplasm, highlighting its high predictive accuracy for pre-breeding applications. High throughput germplasm screening can be done through these developed models to identify trait-specific germplasm, which can be recommended to develop healthy products and thus can also be recommended for production in the farmer field simultaneously.

Optimization of dental adhesive interfaces using tissue biomodulation with DESIGNER biopolymers

OBJECTIVE

To investigate the modulatory effects of four proanthocyanidin-DESIGNERS (PAC-DESIGNERs) on the long-term bond strength of the resin-adhesive interface, the degree of conversion of resin monomers, the chemical-mechanical properties of dentin matrix, and cell biocompatibility.

METHODS

Standardized formulations of PACs with a dominant degree of polymerization - DP (trimers: PM-AB and CV-AB; tetramers: PM-ABA and CV-ABB) were prepared from two sources of AB-Type PACs using a DESIGNER approach. Resin-dentin interface was assessed after 24 hours and 1 year using a microtensile bond strength (µTBS) test. The degree of conversion (DC) of resin monomers and chemical analysis of the dentin matrix were analyzed by ATR-FTIR spectroscopy. The viscoelastic properties of the dentin matrix were assessed by dynamic mechanical analysis (DMA). Cell viability was analyzed using a 3D cell culture model. Data analysis using two- and one-way ANOVA and post-hoc tests (α = 0.05).

RESULTS

All PAC-DESIGNER biomodulation increased the µTBS when compared to control (p < 0.05), regardless of source, DP, and aging. The DC of resin adhesive was not negatively impacted, and an increase in DC was observed with the incorporation of PM-AB and PM-ABA DESIGNERs (p < 0.05). PAC-DESIGNER treatment also increased the dentin matrix complex modulus (153-79 MPa) and storage modulus (151-78 MPa) when compared to control (∼9 MPa, p < 0.05). All DESIGNERs decreased the intensity of amide II/CH2 ratio; a decrease in the amide III/CH2 ratio was observed for CV-ABB (p < 0.05). Moreover, PAC-DESIGNERs exhibited good cell biocompatibility and healthy cell morphology.

SIGNIFICANCE

All PAC-DESIGNERs optimized the dentin-resin µTBS. The different molecular structures played a modulatory role in the chemical-mechanical properties of the dentin matrix, the degree of conversion of adhesive, and cell biocompatibility.

Applications of synchrotron light in seed research: an array of x-ray and infrared imaging methodologies

Synchrotron radiation (SR) provides a wide spectrum of bright light that can be tailored to test myriad research questions. SR provides avenues to illuminate structure and composition across scales, making it ideally suited to the study of plants and seeds. Here, we present an array of methodologies and the data outputs available at a light source facility. Datasets feature seed and grain from a range of crop species including Citrullus sp. (watermelon), Brassica sp. (canola), Pisum sativum (pea), and Triticum durum (wheat), to demonstrate the power of SR for advancing plant science. The application of SR micro-computed tomography (SR-µCT) imaging revealed internal seed microstructures and their three-dimensional morphologies in exquisite detail, without the need for destructive sectioning. Spectroscopy in the infrared spectrum probed sample biochemistry, detailing the spatial distribution of seed macronutrients such as lipid, protein and carbohydrate in the embryo, endosperm and seed coat. Methods using synchrotron X-rays, including X-ray absorption spectroscopy (XAS) and X-ray fluorescence (XRF) imaging revealed elemental distributions, to spatially map micronutrients in seed subcompartments and to determine their speciation. Synchrotron spectromicroscopy (SM) allowed chemical composition to be resolved at the nano-scale level. Diverse crop seed datasets showcase the range of structural and chemical insights provided by five beamlines at the Canadian Light Source, and the potential for synchrotron imaging for informing plant and agricultural research.

Characterization of a novel natural protein-polysaccharide complex from cashew apple bagasse and its functional implications

Cashew apple bagasse (CAB) constituting about 20 % of the cashew apple's (CA) weight, is often overlooked and considered a waste product. This study aims to valorize CAB by extracting and studying a nutritional and functional compounds from CAB, particularly proteins. Response surface methodology (RSM) design and ultrasound-assisted extraction (UAE) are employed to optimize a protein-enriched fraction extraction process. Analysis of CAB-Protein-Pellet composition reveals that its main constituents are sugars (42.49 %) and proteins (22.10 %). HPSEC analysis confirmed the existence of a new natural protein-polysaccharide complex (PPC), an high level of Ara (11.85 g/100 g) and Gal (17.45 g/100 g) indicating the presence of polysaccharides rich in arabinose and galactose (PRAG) with the main class of polymers in the CAB-PPC being AGPs. MIR-FTIR and 1H NMR spectra allowed new insights into the structural features of the PPC derived from CA. The effects of protein-polysaccharide interactions within CAB-PPC on structure and functionality were investigated, revealing interesting functional properties and their correlation relationship. The findings highlight some similarities between CAB-PPC and gum Arabic with minor differences. The interfacial tension of CAB-PPC (21.32 mN/m) was lower than that of gum Arabic (23.71 mN/m). Therefore, CAB-PPC could be suitable for a range of food applications including thickening, stabilization, gelling, water retention, emulsification, and foaming.

Cobalt-induced oxidative stress and defense responses of Adhatoda vasica proliferated shoots

BACKGROUND

Levels of heavy metal pollution are increasing due to industrial activities and urban expansion. While cobalt (Co) can be toxic to plants at high levels and isn't considered essential, it plays a beneficial role in many enzymes and is critical for various biological functions. We conducted experiments to determine how Adhatoda vasica proliferated shoots react to exposure to various Co concentrations (50-1000 µM). We employed physiological and biochemical markers to elucidate the response mechanisms of this medicinal plant. The experiment was conducted in two replicates per treatment. The statistical analysis was based on data from four biological replicates per treatment.

RESULTS

Interestingly, the lowest Co concentration (50 µM) increased proliferated shoot growth by 41.45%. In contrast, higher Co concentrations (100-1000 µM) had detrimental effects on proliferated shoot development, water content, and photosynthetic pigment concentrations. As Co concentration increased, proliferated shoots produced excessive concentrations of reactive oxygen species (ROS). This ROS overproduction is believed to be the primary cause of oxidative damage, as evidenced by the elevated concentrations (18.46%-72.84%) of malondialdehyde (MDA) detected. In response to Co stress, non-enzymatic antioxidants were activated in a concentration-dependent manner. Co administration significantly increased the concentrations of different stress-protective compounds in shoots, including total antioxidants (133.18%), ascorbic acids (217.94%), free and bound phenolics (97.70% and 69.72%, respectively), proline (218.59%), free amino acids (206.96%), soluble proteins (65.97%), and soluble carbohydrates (18.52%). FTIR analysis further corroborated changes in the chemical composition of proliferated shoots. The analysis revealed variations in the peaks associated with major macromolecules, including phenolic compounds, lipids, proteins, carbohydrates, cellulose, hemicellulose, and sugars.

CONCLUSIONS

Our study offers the first comprehensive investigation into mechanisms by which Co stress triggers oxidative damage and alters functional groups in the medicinal plant, Adhatoda vasica.

Impact of cultivar types and thermal processing methods on sweet potato metabolome, a comparative analysis via a multiplex approach of NIR and GC-MS based metabolomics coupled with chemometrics

This study comprehensively analyzes the primary metabolites of sweet potato peels and pulps from four cultivars and assesses the impact of four different processing methods on pulp metabolome using a multiplex metabolomics approach of GC-MS and NIR. A total of 69 metabolites were identified. Beauregard cv. showed the highest sugar content (387.85 mg/g), whereas Sahrawy cv. was higher in alcohols (24.63 mg/g) and organic acids (2.98 mg/g). The chemometric analysis identified key markers that distinguished each cv. represented by its pulp, peel, and processed pulp. KEGG enrichment analysis pinpointed key metabolic pathways leading to the metabolic discrepancy of the specimens. Sugars were the most altered class by processing as manifested by a 5 to 11-fold increase, notably in the air-fried pulp. Air-frying also increased alcohol and organic acid contents. NIR analysis revealed that air-frying was the preferred method of processing, preserving the majority of pulp's metabolites, including β-carotene and phenolics.

Early diagnostics of fibromyalgia: an overview of the challenges and opportunities

INTRODUCTION

Fibromyalgia is a common pain disorder with features of widespread musculoskeletal pain, fatigue, disrupted sleep, cognitive dysfunction, autonomic dysfunction, and mood disorders. Despite its high prevalence and significant impact on quality of life, the diagnosis and management of fibromyalgia remain challenging. Advancements in classification and diagnostics in broad areas have improved our understanding and treatment approach for this condition. We culminate with a discussion of future directions for research into early diagnostics in fibromyalgia.

AREAS COVERED

This perspective examines the current landscape of fibromyalgia biomarker discovery, highlighting challenges that must be addressed and opportunities that are presented as the field evolves.

EXPERT OPINION

Advances in fibromyalgia diagnostics provide an opportunity to dramatically reduce the cost burden placed on health resources for fibromyalgia once we have discovered a reliable reproducible biomarker that is widely accepted among practitioners and patients. Promising results in a number of fields may lead to point of care technologies that will be applicable in the office or bedside without the need for transport to specialized centers. Future research should focus on integrating these various diagnostic approaches to develop a comprehensive, multi-modal diagnostic tool for fibromyalgia.

Energy-Resolved Mass Spectrometry and Mid-Infrared Spectroscopy for Purity Assessment of a Synthetic Peptide Cyclised by Intramolecular Huisgen Click Chemistry

Cyclic peptides have higher stability and better properties as therapeutic agents than their linear peptide analogues. Consequently, intramolecular click chemistry is becoming an increasingly popular method for the synthesis of cyclic peptides from their isomeric linear peptides. However, assessing the purity of these cyclic peptides by mass spectrometry is a significant challenge, as the linear and cyclic peptides have identical masses. In this paper, we have evaluated the analytical capabilities of energy-resolved mass spectrometry (ER MS) and mid-infrared microscopy (IR) to address this challenge. On the one hand, mixtures of both peptides were subjected to collision-induced dissociation tandem mass spectrometry (CID MS/MS) experiments in an ion trap mass spectrometer at several excitation energies. Two different calibration models were used: a univariate model (at a single excitation voltage) and a multivariate model (using multiple excitation voltages). The multivariate model demonstrated slightly enhanced analytical performance, which can be attributed to more effective signal averaging when multiple excitation voltages are considered. On the other hand, IR microscopy was used for the quantification of the relative amount of linear peptide. This was achieved through univariate calibration, based on the absorbance of an alkyne band specific to the linear peptide, and through Partial Least Squares (PLS) multivariate calibration. The PLS calibration model demonstrated superior performance in comparison to univariate calibration, indicating that consideration of the full IR spectrum is preferable to focusing on the specific peak of the linear peptide. The advantage of IR microscopy is that it is linear across the entire working interval, from linear peptide molar ratios of 0 (equivalent to pure cyclic peptide) up to 1 (pure linear peptide). In contrast, the ER MS calibration models exhibited linearity only up to 0.3 linear peptide molar ratio. However, ER MS showed better performances in terms of the limit of detection, intermediate precision and the root-mean-square-error of calibration. Therefore, ER MS is the optimal choice for the detection and quantification of the lowest relative amounts of linear peptides.

Effect of Pre-Sowing Seed Stimulation on Maize Seedling Vigour

The aim of this study was to investigate the effects of treating maize (Zea mays L.) seeds with fish collagen hydrolysate (FC) and keratin (KE) derived from animal waste by-products of leather and meat production, as well as poly(hexamethylene biguanide) hydrochloride (P) and bentonite (B). This research is in line with the search for new, environmentally friendly methods to increase yields of industrial crops in a way that is compatible with sustainable development. The effect of the binders used was investigated by analysing the grown maize seedlings by determining changes in parameters of chlorophyll fluorescence, photosynthetic pigments, elemental composition and FTIR analysis on maize shoots. The results indicated a slightly higher fresh weight (FW) of shoots in plants treated with fish collagen, PHMB and bentonite (FC+P+B) and FW of roots in plants treated with keratin, PHMB and bentonite (KE+P+B). Unexpectedly, the FW and dry weight (DW) of both roots and shoots of all bentonite-treated plants were significantly higher than the corresponding non-bentonite-treated groups. In addition, changes in chlorophyll-a fluorescence were observed for the keratin, PHMB and bentonite variants. This study showed that the proposed materials could be promising seed pelleting agents to improve seed growth and yield.

Application of adaptive chaotic dung beetle optimization algorithm to near-infrared spectral model transfer

A new transfer approach was proposed to share calibration models of the hexamethylenetetramine-acetic acid solution for studying hexamethylenetetramine concentration values across different near-infrared (NIR) spectrometers. This approach combines Savitzky-Golay first derivative (S_G_1) and orthogonal signal correction (OSC) preprocessing, along with feature variable optimization using an adaptive chaotic dung beetle optimization (ACDBO) algorithm. The ACDBO algorithm employs tent chaotic mapping and a nonlinear decreasing strategy, enhancing the balance between global and local search capabilities and increasing population diversity to address limitations observed in traditional dung beetle optimization (DBO). Validated using the CEC-2017 benchmark functions, the ACDBO algorithm demonstrated superior convergence speed, accuracy, and stability. In the context of a partial least squares (PLS) regression model for transferring hexamethylenetetramine-acetic acid solutions using NIR spectroscopy, the ACDBO algorithm excelled over alternative methods such as uninformative variable elimination, competitive adaptive reweighted sampling, cuckoo search, grey wolf optimizer, differential evolution, and DBO in efficiency, accuracy of feature variable selection, and enhancement of model predictive performance. The algorithm attained outstanding metrics, including a determination coefficient for the calibration set (Rc2) of 0.99999, a root mean square error for the calibration set (RMSEC) of 0.00195%, a determination coefficient for the validation set (Rv2) of 0.99643, a root mean squared error for the validation set (RMSEV) of 0.03818%, residual predictive deviation (RPD) of 16.72574. Compared to existing OSC, slope and bias correction (S/B), direct standardization (DS), and piecewise direct standardization (PDS) model transfer methods, the novel strategy enhances the accuracy and robustness of model predictions. It eliminates irrelevant background information about the hexamethylenetetramine concentration, thereby minimizing the spectral discrepancies across different instruments. As a result, this approach yields a determination coefficient for the prediction set (Rp2) of 0.96228, a root mean squared error for the prediction set (RMSEP) of 0.12462%, and a relative error rate (RER) of 17.62331, respectively. These figures closely follow those obtained using DS and PDS, which recorded Rp2, RMSEP, and RER values of 0.97505, 0.10135%, 21.67030, and 0.98311, 0.08339%, 26.33552, respectively. Unlike conventional methods such as OSC, S/B, DS, and PDS, this novel approach does not require the analysis of identical samples across different instruments. This characteristic significantly broadens its applicability for model transfer, which is particularly beneficial for transferring specific measurement samples.

Freestanding Germanium Photonic Crystal Waveguide for a Highly Sensitive and Compact Mid-Infrared On-Chip Gas Sensor

The performance of mid-infrared (MIR) on-chip gas sensors, operating via laser absorption spectroscopy, hinges critically on light-matter interaction dynamics, significantly influenced by external confinement and the effective light path length. Conventional on-chip sensors, however, face challenges in achieving the required limit of detection for highly sensitive applications, primarily due to their intrinsically short effective light path. Furthermore, these sensors are limited in their spectral range coverage within the MIR spectrum by the constraints of standard silicon-based platforms. To overcome these limitations, our research presents a novel approach to fabricate a freestanding germanium (Ge) photonic crystal waveguide (PCW) on a germanium-on-insulator (Ge-OI) platform, utilizing yttrium oxide (Y2O3) as the buried oxide layer. This device leverages the broad transparent windows of Ge and Y2O3, broadening the spectral coverage across the MIR range. The introduction of the PCW and its slow light effect significantly elevate external confinement and light-matter interactions, enabling a notable reduction in waveguide length, which traditionally limits on-chip configurations. The freestanding structure not only expands the sensing region and enhances external confinement but also prevents the emergence of leaky modes within the PCW. As a result, our compact sensor achieves an exceptionally low LoD of 7.56 ppm for carbon dioxide (CO2) sensing at the operational wavelength of 4.23 μm, with a compact waveguide length of only 800 μm.

Digital model of biochemical reactions in lactic acid bacterial fermentation of simple glucose and biowaste substrates

As concerns about the environmental impacts of biowaste disposal increase, lactic acid bacterial fermentation is becoming increasingly popular. Current academic research is aimed at the process optimization by developing digital bioreactors. The primary focus is to develop a digital model mimicking the biochemical reactions. In the light of this, this paper intended to build a digital model of biochemical reactions during the fermentation process of both glucose and biowaste substrates, including white pasta and organic municipal waste. For this purpose, near-infrared (NIR) and mid-infrared (MIR) spectroscopy techniques were used to collect spectral information during the fermentation process. Next, the samples were analyzed by High Pressure Liquid Chromatography (HPLC) to measure their glucose, fructose, arabinose, xylose, disaccharide, lactic acid, and acetic acid contents. The results showed that learning algorithms trained on MIR spectra accurately estimated the biochemical reactions for both glucose and biowaste substrates. For the glucose substrate, the results showed R-squared of 0.97 and RMSE of 4.69 g/L for glucose, and R-squared of 0.98 and RMSE of 2.74 g/L for lactic acid. In the case of biowaste substrate, estimations included glucose (R-squared = 0.97, RMSE = 4.69 g/L), fructose (R-squared = 0.88, RMSE = 1.47 g/L), arabinose (R-squared = 0.98, RMSE = 0.55 g/L), xylose (R-squared = 0.93, RMSE = 1.11 g/L), disaccharide (R-squared = 0.90, RMSE = 0.55 g/L), total sugar (R-squared = 0.98, RMSE = 3.79 g/L), lactic acid (R-squared = 0.98, RMSE = 2.74 g/L), and acetic acid (R-squared = 0.97, RMSE = 0.36 g/L). Regarding NIR spectral data, the predictive models were accurate when the substrate was glucose, however, they failed to accurately estimate the chemical reactions in the case of biowaste substrate. The findings of this study can be used to fulfill the requirements for a continuous fermentation process with the objective of maximizing lactic acid production.

Extending mid-infrared wavelength to 6.84 μm in oxide nonlinear optical crystal via birefringence dispersion management

Extending lasing wavelengths to the mid-infrared (MIR) spectrum is vital for both civilian and military applications; however, it remains challenging when employing oxide nonlinear optical crystals. In this study, we report the generation of MIR nanosecond pulses via difference frequency generation (DFG) with a near-IR pump using a newly designed langasite (LGS) crystal, La3(Nb0.6Ta0.4)0.5Ga5.5O14 (LGNT0.4), which incorporates birefringence dispersion management techniques with La3Ga5.5Nb0.5O14 (LGN) as a template. Due to the improved effective nonlinear coefficients and the maintained IR cutoff relative to LGN, the tunable DFG laser in LGNT0.4 extended from 4.24 to 6.84 μm, delivering a maximum pulse energy of 16.3 μJ at 5.02 μm. To the best of our knowledge, this is the first known oxide material capable of generating tunable nanosecond pulsed lasers beyond 6 μm at μJ-level energies, demonstrating promising potential for high-intensity MIR laser systems owing to its high laser damage threshold.

Hyperspectral reflectance imaging for visualizing reducing sugar content, moisture, and hollow rate in red ginseng

Red ginseng (RG) has been traditionally valued in Northeast Asia for its health-enhancing properties. Recent advancements in hyperspectral imaging (HSI) offer a non-destructive, efficient, and reliable method to assess critical quality indicators of RG, such as reducing sugar content (RSC), water content (WC), and hollow rate (HR). This study developed predictive models using HSI technology to monitor these quality indicators over the spectral range of 400-1700 nm. Image features were enhanced using Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF), followed by classification through Spectral Angle Mapping (SAM). The best-performing model for RSC achieved an R2 value of 0.6198 and a root mean square error (RMSE) of 0.013. For WC, the optimal model obtained an R2 value of 0.6555 and an RMSE of 0.014. The spatial distribution of RSC, WC, and HR was effectively visualized, demonstrating the potential of HSI for on-site quality control of RG. This study provides a foundation for real-time, non-invasive monitoring of RG quality, addressing industry needs for rapid and reliable assessment methods.

Combining Fourier-transform infrared spectroscopy and multivariate analysis for chemotyping of cell wall composition in Mungbean (Vigna radiata (L.) Wizcek)

BACKGROUND

Dissection of complex plant cell wall structures demands a sensitive and quantitative method. FTIR is used regularly as a screening method to identify specific linkages in cell walls. However, quantification and assigning spectral bands to particular cell wall components is still a major challenge, specifically in crop species. In this study, we addressed these challenges using ATR-FTIR spectroscopy as it is a high throughput, cost-effective and non-destructive approach to understand the plant cell wall composition. This method was validated by analysing different varieties of mungbean which is one of the most important legume crops grown widely in Asia.

RESULTS

Using standards and extraction of a specific component of cell wall components, we assigned 1050-1060 cm-1 and 1390-1420 cm-1 wavenumbers that can be widely used to quantify cellulose and lignin, respectively, in Arabidopsis, Populus, rice and mungbean. Also, using KBr as a diluent, we established a method that can relatively quantify the cellulose and lignin composition among different tissue types of the above species. We further used this method to quantify cellulose and lignin in field-grown mungbean genotypes. The ATR-FTIR-based study revealed the cellulose content variation ranges from 27.9% to 52.3%, and the lignin content variation ranges from 13.7% to 31.6% in mungbean genotypes.

CONCLUSION

Multivariate analysis of FT-IR data revealed differences in total cell wall (600-2000 cm-1), cellulose (1000-1100 cm-1) and lignin (1390-1420 cm-1) among leaf and stem of four plant species. Overall, our data suggested that ATR-FTIR can be used for the relative quantification of lignin and cellulose in different plant species. This method was successfully applied for rapid screening of cell wall composition in mungbean stem, and similarly, it can be used for screening other crops or tree species.

Modelling and numerical methods for identifying low-level adulteration in ground beef using near-infrared hyperspectral imaging (NIR-HSI)

Owing to the inherent characteristics of ground beef, adulteration presents a substantial risk for suppliers and consumers alike. This study developed a robust and novel method for identifying replacement fraud in ground beef with beef liver, beef heart, and pork using Near Infrared-Hyperspectral Imaging (NIR-HSI) coupled with chemometric and other statistical methods. More specifically, NIR-HSI provided an efficient and accurate means of identifying each type of adulteration using the classification model Genetic Algorithm (GA) - Backpropagation Artificial Neural Network (BPANN), showing perfect sensitivity and specificity (a value of 1.00) for the calibration and the validation sets for all types of adulteration. As an alternative to chemometric analysis, Hyperspectral Imaging-Root Mean Square (HSI-RMS) value, based on the RMScut-off calculation, was determined to discriminate types of adulterations without the need of resource-intensive modelling. This HSI-RMS approach provides a simple-to-use method that avoids the complexity of HSI data processing and aims to directly understand the similarity between different spectra of one sample in the pixel level. Different types of adulteration show noticeable differences reflected in the HSI-RMS value (varying from 55 to 1439), which demonstrate the potential of HSI-RMS concept as a novel and valuable alternative for assessing the HSI data and facilitating the identification of adulterants.

Assessment of Fatty Acid and Oxylipin Profile of Resprouting Olive Trees Positive to Xylella fastidiosa subsp. pauca in Salento (Apulia, Italy)

Xylella fastidiosa subsp. pauca ST53 (XFP), the causal agent of olive quick decline syndrome (OQDS), was thoroughly investigated after a 2013 outbreak in the Salento region of Southern Italy. Some trees from Ogliarola Salentina and Cellina di Nardò, susceptible cultivars in the Gallipoli area, the first XFP infection hotspot in Italy, have resprouted crowns and are starting to flower and yield fruits. Satellite imagery and Normalized Difference Vegetation Index analyses revealed a significant improvement in vegetation health and productivity from 2018 to 2022 of these trees. Lipid molecules have long been recognized as plant defense modulators, and recently, we investigated their role in XFP-positive hosts and in XFP-resistant as well as in XFP-susceptible cultivars of olive trees. Here, we present a case study regarding 36 olive trees (12 XFP-positive resprouting, 12 XFP-positive OQDS-symptomatic, and 12 XFP-negative trees) harvested in 2022 within the area where XFP struck first, killing millions of trees in a decade. These trees were analyzed for some free fatty acid, oxylipin, and plant hormones, in particular jasmonic and salicylic acid, by targeted LC-MS/MS. Multivariate analysis revealed that lipid markers of resistance (e.g., 13-HpOTrE), along with jasmonic and salicylic acid, were accumulated differently in the XFP-positive resprouting trees from both cultivars with respect to XFP-positive OQDS symptomatic and XFP-negative trees, suggesting a correlation of lipid metabolism with the resprouting, which can be an indication of the resiliency of these trees to OQDS. This is the first report concerning the resprouting of OQDS-infected olive trees in the Salento area.

Impact of pilot-scale microfluidization on soybean protein structure in powder and solution

The effect of microfluidization treatment on the primary, secondary, and tertiary structure of soybean protein isolate (SPI) was investigated. The samples were treated with and without controlling the temperature and circulated in the system 1, 3, and 5 times at high pressure (137 MPa). Then, the treated samples were freeze-dried and reconstituted in water to check the impact of the microfluidization on two different states: powder and solution. Regarding the primary structure, the SDS-PAGE analysis under reducing conditions showed that the protein bands remained unchanged when exposed to microfluidization treatment. When the temperature was controlled for the samples in their powder state, a significant decrease in the quantities of β-sheet and random coil and a slight reduction in α-helix content was noticed. The observed decrease in β-sheet and the increase in β-turns in treated samples indicated that microfluidization may lead to protein unfolding, opening the hydrophobic regions. Additionally, a lower amount of α-helix suggests a higher protein flexibility. After reconstitution in water, a significant difference was observed only in α-helix, β-sheet and β-turn. Related to the tertiary structure, microfluidization increases the surface hydrophobicity. Among all the conditions tested, the samples where the temperature is controlled seem the most suitable.

Implementation of multiomic mass spectrometry approaches for the evaluation of human health following environmental exposure

Omics analyses collectively refer to the possibility of profiling genetic variants, RNA, epigenetic markers, proteins, lipids, and metabolites. The most common analytical approaches used for detecting molecules present within biofluids related to metabolism are vibrational spectroscopy techniques, represented by infrared, Raman, and nuclear magnetic resonance (NMR) spectroscopies and mass spectrometry (MS). Omics-based assessments utilizing MS are rapidly expanding and being applied to various scientific disciplines and clinical settings. Most of the omics instruments are operated by specialists in dedicated laboratories; however, the development of miniature portable omics has made the technology more available to users for field applications. Variations in molecular information gained from omics approaches are useful for evaluating human health following environmental exposure and the development and progression of numerous diseases. As MS technology develops so do statistical and machine learning methods for the detection of molecular deviations from personalized metabolism, which are correlated to altered health conditions, and they are intended to provide a multi-disciplinary overview for researchers interested in adding multiomic analysis to their current efforts. This includes an introduction to mass spectrometry-based omics technologies, current state-of-the-art capabilities and their respective strengths and limitations for surveying molecular information. Furthermore, we describe how knowledge gained from these assessments can be applied to personalized medicine and diagnostic strategies.

Influence of sample preparation methods on FTIR spectra for taxonomic identification of tropical trees in the Atlantic forest

The Atlantic forest is one of the world's major tropical biomes due to its rich biodiversity. Its vast diversity of plant species poses challenges in floristic surveys. Fourier transform infrared spectroscopy (FTIR) enables rapid and residue-free data collection, providing diverse applications in organic sample analysis. FTIR spectra quality depends on the sample preparation methodology. However, no research on FTIR spectroscopy methodology for taxonomy has been conducted with tropical tree species. Hence, this study addresses the sample preparation influence on FTIR spectra for the taxonomic classification of 12 tree species collected in the Serra do Mar State Park (PESM) - Cunha Nucleus - São Paulo State, Brazil. Spectra were obtained from intact fresh (FL), intact dried (DL), and heat-dried ground (GL) leaves. The spectra were evaluated through chemometrics using Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), and Linear Discriminant Analysis (LDA) with validation by LDA-PCA. The results demonstrate that sample preparation directly influences tropical species FTIR spectra categorization capability. The best taxonomic classification result for all techniques, validated by LDA-PCA, was obtained from GL. FTIR spectra evaluation through PCA, HCA, and LDA allow for the observation of phylogenetic relationships among the species. FTIR spectroscopy proves to be a viable technique for taxonomic evaluation of tree species in floristic exploration of tropical biomes which can complement traditional tools used for taxonomic studies.

Handheld hyperspectral imaging as a tool for the post-mortem interval estimation of human skeletal remains

In forensic medicine, estimating human skeletal remains' post-mortem interval (PMI) can be challenging. Following death, bones undergo a series of chemical and physical transformations due to their interactions with the surrounding environment. Post-mortem changes have been assessed using various methods, but estimating the PMI of skeletal remains could still be improved. We propose a new methodology with handheld hyperspectral imaging (HSI) system based on the first results from 104 human skeletal remains with PMIs ranging between 1 day and 2000 years. To differentiate between forensic and archaeological bone material, the Convolutional Neural Network analyzed 65.000 distinct diagnostic spectra: the classification accuracy was 0.58, 0.62, 0.73, 0.81, and 0.98 for PMIs of 0 week-2 weeks, 2 weeks-6 months, 6 months-1 year, 1 year-10 years, and >100 years, respectively. In conclusion, HSI can be used in forensic medicine to distinguish bone materials >100 years old from those <10 years old with an accuracy of 98%. The model has adequate predictive performance, and handheld HSI could serve as a novel approach to objectively and accurately determine the PMI of human skeletal remains.

Fast and low-cost method for direct and simultaneous determination of nitrogen and carbon in soybean leaves using benchtop and portable near-infrared devices

BACKGROUND

The current techniques for determining carbon and nitrogen content to provide information about the nutritional status of plants are time-consuming and expensive. For this reason, the objective of this study was to develop an analytical method for the direct and simultaneous determination of nitrogen and carbon elemental content in soybean leaves using near-infrared spectroscopy and compare the performance of conventional (1100-2500 nm spectral range) and portable equipment (1100-1700 nm spectral range). Partial least-squares regression models were developed using 27 soybean leaf samples collected during the 2021 harvest and applied for the simultaneous determination of carbon and nitrogen in 13 samples collected during the 2022 harvest.

RESULTS

The root-mean-square error of prediction values for nitrogen and carbon were low (2.42 g kg-1 and 4.37 g kg-1 respectively) for the benchtop method yielded low but higher for the portable method (3.82 g kg-1 and 10.7 g kg-1 respectively). The benchtop method did not show significant differences when compared with the reference method for determining nitrogen and carbon. In contrast, the portable methodology showed potential as a screening method for determining nitrogen levels, particularly in fieldwork.

CONCLUSION

The methodologies evaluated in this study were implemented and evaluated under real crop monitoring conditions, using independent sets of calibration and prediction samples. Their utilization enables the acquisition of cost-effective, safe analytical data aligning with the principles of green analytical chemistry. © 2023 Society of Chemical Industry.

Online monitoring of phytate content in plant residuals during wet-treatment

The occurrence of organically bound phosphorus (P) as phytate in plant-based feeding material is a challenge for livestock farming due to limited utilization during the digestion by the animal. Its excretion into the environment through the manure pathway, poses a challenge, due to increased eutrophication and restrictions for P. Hence, while the routine supplementation of phytase enzymes in monogastric diets is common practice, metabolically triggering endogenous plant enzymes by wet-treatment prior to feeding can also lead to a better utilization of phytate bound P and increased digestibility by the animal. Nonetheless, traditional quantification of residual phytate content in plant material is both labor- and chemical-intense. The aim of this study is, therefore, to predict the remaining phytate content during wet-treatment through a straightforward and flexible methodological approach based on real-time analysis. For this, rye bran is used as a model substrate. A partial least squares regression algorithm relates the infrared spectra to the concentrations and predict the amount of P species that are transferred from the bran matrix to the liquid phase. By applying a mass balance for P and considering the effect of water compression, the amount of residual phytate content in rye bran at different time points of wet-treatment is determined. Results are compared to wet chemical methods, resulting in a RMSEP of 0.28 gphytate∙100 gbran-1. In addition, the study demonstrates the feasibility of this approach and provides insights into phytate degradation in plant residuals. The method holds the potential for further applications for the screening and investigation of feed material conditioning and also offers the possibility to employ various real-time analytical techniques for assessing phytate remnants in biological samples during wet-treatment.

Applications of some advanced sequencing, analytical, and computational approaches in medicinal plant research: a review

The potential active chemicals found in medicinal plants, which have long been employed as natural medicines, are abundant. Exploring the genes responsible for producing these compounds has given new insights into medicinal plant research. Previously, the authentication of medicinal plants was done via DNA marker sequencing. With the advancement of sequencing technology, several new techniques like next-generation sequencing, single molecule sequencing, and fourth-generation sequencing have emerged. These techniques enshrined the role of molecular approaches for medicinal plants because all the genes involved in the biosynthesis of medicinal compound(s) could be identified through RNA-seq analysis. In several research insights, transcriptome data have also been used for the identification of biosynthesis pathways. miRNAs in several medicinal plants and their role in the biosynthesis pathway as well as regulation of the disease-causing genes were also identified. In several research articles, an in silico study was also found to be effective in identifying the inhibitory effect of medicinal plant-based compounds against virus' gene(s). The use of advanced analytical methods like spectroscopy and chromatography in metabolite proofing of secondary metabolites has also been reported in several recent research findings. Furthermore, advancement in molecular and analytic methods will give new insight into studying the traditionally important medicinal plants that are still unexplored.

Deep learning analysis of mid-infrared microscopic imaging data for the diagnosis and classification of human lymphomas

The present study presents an alternative analytical workflow that combines mid-infrared (MIR) microscopic imaging and deep learning to diagnose human lymphoma and differentiate between small and large cell lymphoma. We could show that using a deep learning approach to analyze MIR hyperspectral data obtained from benign and malignant lymph node pathology results in high accuracy for correct classification, learning the distinct region of 3900 to 850 cm-1 . The accuracy is above 95% for every pair of malignant lymphoid tissue and still above 90% for the distinction between benign and malignant lymphoid tissue for binary classification. These results demonstrate that a preliminary diagnosis and subtyping of human lymphoma could be streamlined by applying a deep learning approach to analyze MIR spectroscopic data.

Natural complex substances: From molecules to the molecular complexes. Analytical and technological advances for their definition and differentiation from the corresponding synthetic substances

Natural complex substances (NCSs) are a heterogeneous family of substances that are notably used as ingredients in several products classified as food supplements, medical devices, cosmetics and traditional medicines, according to the correspondent regulatory framework. The compositions of NCSs vary widely and hundreds to thousands of compounds can be present at the same time. A key concept is that NCSs are much more than the simple sum of the compounds that constitute them, in fact some emerging phenomena are the result of the supramolecular interaction of the constituents of the system. Therefore, close attention should be paid to produce and characterize these systems. Today many natural compounds are produced by chemical synthesis and are intentionally added to NCSs, or to formulated natural products, to enhance their properties, lowering their production costs. Market analysis shows a tendency of people to use products made with NCSs and, currently, products made with ingredients of natural origin only are not conveniently distinguishable from those containing compounds of synthetic origin. Furthermore, the uncertainty of the current European regulatory framework does not allow consumers to correctly differentiate and identify products containing only ingredients of natural origin. The high demand for specific and effective NCSs and their high-cost offer on the market, create the conditions to economically motivated sophistications, characterized by the addition of a cheap material to a more expensive one, just to increase profit. This type of practice can concern both the addition of less valuable natural materials and the addition of pure artificial compounds with the same structure as those naturally present. In this scenario, it becomes essential for producers of natural products to have advanced analytical techniques to evaluate the effective naturalness of NCSs. In fact, synthetically obtained compounds are not identical to their naturally occurring counterparts, due to the isotopic composition or chirality, as well as the presence of different trace metabolites (since pure substances in nature do not exist). For this reason, in this review, the main analytical tests that can be performed to differentiate natural compounds from their synthetic counterparts will be highlighted and the main analytical technologies will be described. At the same time, the main fingerprint techniques useful for characterizing the complexity of the NCSs, also allowing their identification and quali-quantitative evaluation, will be described. Furthermore, NCSs can be produced through different manufacturing processes, not all of which are on the same level of quality. In this review the most suitable technologies for green processes that operate according to physical extraction principles will be presented, as according to the authors they are the ones that come closest to creating more life-cycle compatible NCSs and that are well suited to the European green deal, a strategy with the aim of transforming the EU into a sustainable and resource-efficient society by 2050.

Chemometric Analysis for the Prediction of Biochemical Compounds in Leaves Using UV-VIS-NIR-SWIR Hyperspectroscopy

Reflectance hyperspectroscopy is recognised for its potential to elucidate biochemical changes, thereby enhancing the understanding of plant biochemistry. This study used the UV-VIS-NIR-SWIR spectral range to identify the different biochemical constituents in Hibiscus and Geranium plants. Hyperspectral vegetation indices (HVIs), principal component analysis (PCA), and correlation matrices provided in-depth insights into spectral differences. Through the application of advanced algorithms-such as PLS, VIP, iPLS-VIP, GA, RF, and CARS-the most responsive wavelengths were discerned. PLSR models consistently achieved R2 values above 0.75, presenting noteworthy predictions of 0.86 for DPPH and 0.89 for lignin. The red-edge and SWIR bands displayed strong associations with pivotal plant pigments and structural molecules, thus expanding the perspectives on leaf spectral dynamics. These findings highlight the efficacy of spectroscopy coupled with multivariate analysis in evaluating the management of biochemical compounds. A technique was introduced to measure the photosynthetic pigments and structural compounds via hyperspectroscopy across UV-VIS-NIR-SWIR, underpinned by rapid multivariate PLSR. Collectively, our results underscore the burgeoning potential of hyperspectroscopy in precision agriculture. This indicates a promising paradigm shift in plant phenotyping and biochemical evaluation.

Spectroscopic Analyses Highlight Plant Biostimulant Effects of Baker's Yeast Vinasse and Selenium on Cabbage through Foliar Fertilization

The main aim of this study is to find relevant analytic fingerprints for plants' structural characterization using spectroscopic techniques and thermogravimetric analyses (TGAs) as alternative methods, particularized on cabbage treated with selenium-baker's yeast vinasse formulation (Se-VF) included in a foliar fertilizer formula. The hypothesis investigated is that Se-VF will induce significant structural changes compared with the control, analytically confirming the biofortification of selenium-enriched cabbage as a nutritive vegetable, and particularly the plant biostimulant effects of the applied Se-VF formulation on cabbage grown in the field. The TGA evidenced a structural transformation of the molecular building blocks in the treated cabbage leaves. The ash residues increased after treatment, suggesting increased mineral accumulation in leaves. X-ray diffraction (XRD) and Fourier-transform infrared spectroscopy (FTIR) evidenced a pectin-Iα-cellulose structure of cabbage that correlated with each other in terms of leaf crystallinity. FTIR analysis suggested the accumulation of unesterified pectin and possibly (seleno) glucosinolates and an increased network of hydrogen bonds. The treatment with Se-VF formulation induced a significant increase in the soluble fibers of the inner leaves, accompanied by a decrease in the insoluble fibers. The ratio of soluble/insoluble fibers correlated with the crystallinity determined by XRD and with the FTIR data. The employed analytic techniques can find practical applications as fast methods in studies of the effects of new agrotechnical practices, while in our particular case study, they revealed effects specific to plant biostimulants of the Se-VF formulation treatment: enhanced mineral utilization and improved quality traits.

Single-mode optical fiber couplers made of fluoride glass

We demonstrate the first single-mode optical fiber couplers made with ZBLAN optical fiber. Couplers are fabricated using a controlled tapering procedure enabling high reproducibility while limiting glass crystallization. A coupling ratio of up to 41%/59% in cross/through ports with an excess loss of 2.5 dB is obtained at a wavelength of 2.73 μm. In addition, the stability of a coupler with traces of surface crystallization is tested at ambient atmosphere over a period of more than 90 days.

Synchronously Predicting Tea Polyphenol and Epigallocatechin Gallate in Tea Leaves Using Fourier Transform-Near-Infrared Spectroscopy and Machine Learning

Tea polyphenol and epigallocatechin gallate (EGCG) were considered as key components of tea. The rapid prediction of these two components can be beneficial for tea quality control and product development for tea producers, breeders and consumers. This study aimed to develop reliable models for tea polyphenols and EGCG content prediction during the breeding process using Fourier Transform-near infrared (FT-NIR) spectroscopy combined with machine learning algorithms. Various spectral preprocessing methods including Savitzky-Golay smoothing (SG), standard normal variate (SNV), vector normalization (VN), multiplicative scatter correction (MSC) and first derivative (FD) were applied to improve the quality of the collected spectra. Partial least squares regression (PLSR) and least squares support vector regression (LS-SVR) were introduced to establish models for tea polyphenol and EGCG content prediction based on different preprocessed spectral data. Variable selection algorithms, including competitive adaptive reweighted sampling (CARS) and random forest (RF), were further utilized to identify key spectral bands to improve the efficiency of the models. The results demonstrate that the optimal model for tea polyphenols calibration was the LS-SVR with R_p = 0.975 and RPD = 4.540 based on SG-smoothed full spectra. For EGCG detection, the best model was the LS-SVR with R_p = 0.936 and RPD = 2.841 using full original spectra as model inputs. The application of variable selection algorithms further improved the predictive performance of the models. The LS-SVR model for tea polyphenols prediction with R_p = 0.978 and RPD = 4.833 used 30 CARS-selected variables, while the LS-SVR model build on 27 RF-selected variables achieved the best predictive ability with R_p = 0.944 and RPD = 3.049, respectively, for EGCG prediction. The results demonstrate a potential of FT-NIR spectroscopy combined with machine learning for the rapid screening of genotypes with high tea polyphenol and EGCG content in tea leaves.

Reflectance spectroscopy as a promising tool for 'sensing' metals in hyperaccumulator plants

MAIN CONCLUSION

The VNIR reflectance spectra of nickel hyperaccumulator plant leaves have spectral variations due to high nickel concentrations and this property could potentially be used for discovery of these plants.  Hyperaccumulator plants accumulate high concentrations of certain metals, including manganese, cobalt, or nickel. Of these metals, the divalent ions of nickel have three absorption bands in the visible to near-infrared region which may cause variations in the spectral reflectance of nickel hyperaccumulator plant leaves, but this has not been investigated previously. In this shortproof-of-concept study, the spectral reflectance of eight different nickel hyperaccumulator plant species leaves were subjected to visible and near-infrared and shortwave infrared (VNIR-SWIR) reflectance spectrum measurements in dehydrated state, and for one species, it was also assessed in hydrated state. Nickel concentrations in the plant leaves were determined with other methods and then correlated to the spectral reflectance data. Spectral variations centred at 1000 ± 150 nm were observed and had R-values varying from 0.46 to 0.96 with nickel concentrations. The extremely high nickel concentrations in nickel hyperaccumulator leaves reshape their spectral reflectance features, and the electronic transition of nickel-ions directly contributes to absorption at ~ 1000 nm. Given that spectral variations are correlated with nickel concentrations it make VNIR-SWIR reflectance spectrometry a potential promising technique for discovery of hyperaccumulator plants, not only in the laboratory or herbarium, but also in the field using drone-based platforms. This is a preliminary study which we hope will instigate further detailed research on this topic to validate the findings and to explore possible applications.

Characterization of hybrid larch somatic embryo maturation by biochemical analyses and by a novel, fast mid-infrared approach

The morphology of somatic embryos (SE) is not a sufficient criterion to determine the level of maturation and the optimal stage to transfer embryos for germination, unlike the biochemical components. This composition characterization in the laboratory is too restrictive to be considered at each maturation cycle, as would be necessary. It is, therefore, essential to consider alternative methods. The objectives of this work were to achieve a complete biochemical characterization of the embryos during their development, to serve as a reference and develop a characterization based on infrared spectrometry and chemometrics. During the precotyledonary stage (0-3 weeks of maturation), water content and glucose and fructose levels were high, which is consistent with SE development. After 4 weeks, the cotyledonary SE had a metabolism oriented towards the storage accumulation of lipids, proteins and starch, whereas raffinose only appeared from 8 weeks. Mid-infrared calibration models were developed for water, proteins, lipids, carbohydrates, glucose, fructose, inositols, raffinose, stachyose and starch contents with an r2 average of 0.84. A model was also developed to discriminate the weeks of SE maturation. Different classes of age were discriminated with at least 72% of accuracy. Infrared analysis of the SE based on their full biochemical spectral fingerprint revealed a very slight variation in composition between 7 and 9 weeks, information that is very difficult to obtain by conventional analysis methods. These results provide novel insights into the maturation of conifer SE and indicate that mid-infrared spectrometry could be an easy and effective method for SE characterization.

Potential of Near-Infrared Spectroscopy (NIRS) for Efficient Classification Based on Postharvest Storage Time, Cultivar and Maturity in Coconut Water

Coconut water (CW) is a popular and healthful beverage, and ensuring its quality is crucial for consumer satisfaction. This study aimed to explore the potential of near-infrared spectroscopy (NIRS) and chemometric methods for analyzing CW quality and distinguishing samples based on postharvest storage time, cultivar, and maturity. CW from nuts of Wenye No. 2 and Wenye No. 4 cultivars in China, with varying postharvest storage time and maturities, were subjected to NIRS analysis. Partial least squares regression (PLSR) models were developed to predict reducing sugar and soluble sugar contents, revealing moderate applicability but lacking accuracy, with the residual prediction deviation (RPD) values ranging from 1.54 to 1.83. Models for TSS, pH, and TSS/pH exhibited poor performance with RPD values below 1.4, indicating limited predictability. However, the study achieved a total correct classification rate exceeding 95% through orthogonal partial least squares discriminant analysis (OPLS-DA) models, effectively discriminating CW samples based on postharvest storage time, cultivar, and maturity. These findings highlight the potential of NIRS combined with appropriate chemometric methods as a valuable tool for analyzing CW quality and efficiently distinguishing samples. NIRS and chemometric techniques enhance quality control in coconut water, ensuring consumer satisfaction and product integrity.

Effect of atmospheric pin-to-plate cold plasma on oat protein: Structural, chemical, and foaming characteristics

The impact of novel pin-to-plate atmospheric cold plasma was investigated with input voltage (170 V, 230 V) and exposure time (15 & 30 min) on oat protein by studying structural (FTIR, circular dichroism (CD), UV-vis, Fluorescence), morphological (particle size analysis, SEM, turbidity), chemical (pH, redox potential (ORP), ζ potential, carbonyl, sulfhydryl, surface hydrophobicity), and foaming characteristics. The plasma treatment reduced the pH while increasing the ORP of the dispersions. These ionic environment changes affected the ζ potential and particle size leading to the formation of larger aggregates (170-15; 230-15) and distorted smaller ones (170-30; 230-30) as confirmed by SEM. The FTIR spectra showed reduced intensity at specific amide bands (1600-1700 cm^-1) and also an increase in carbonyl stretching (1743 cm^-1) representing oxidative carbonylation (increase in carbonyl content). Thus, the partial exposure of hydrophobic amino acids increases surface hydrophobicity. The altered secondary structure (rise in α-helix, decrement in β-sheets and turns), and tertiary structures were observed in circular dichroism (CD) and UV absorbance and fluorescence characteristics of proteins respectively. Furthermore, the increase in free sulfhydryl content and disulfide content was highly affected by the plasma treatments due to observed protein unfolding and aggregations. Besides, the increased solubility and reduced surface tension contributed to the improved foaming characteristics. Thus, plasma processing influences protein structure affecting their characteristics and other functionalities.

Applications of Hyperspectral Imaging Technology Combined with Machine Learning in Quality Control of Traditional Chinese Medicine from the Perspective of Artificial Intelligence: A Review

Traditional Chinese medicine (TCM) is the treasure of China, and the quality control of TCM is of crucial importance. In recent years, with the quick rise of artificial intelligence (AI) and the rapid development of hyperspectral imaging (HSI) technology, the combination of the two has been widely used in the quality evaluation of TCM. Machine learning (ML) is the core wisdom of AI, and its progress in rapid analysis and higher accuracy improves the potential of applying HSI to the field of TCM. This article reviewed five aspects of ML applied to hyperspectral data analysis of TCM: partition of data set, data preprocessing, data dimension reduction, qualitative or quantitative models, and model performance measurement. The different algorithms proposed by researchers for quality assessment of TCM were also compared. Finally, the challenges in the analysis of hyperspectral images for TCM were summarized, and the future works were prospected.

Characterization of Mineral Composition and Nutritional Value of Acacia Green Pods

The Acacia genus is considered one of the most invasive taxa in some habitats, namely coastal dunes, maritime calcareous soils, fresh lands in the valleys, mountainous areas, and the banks of watercourses and roadsides. In Portugal, the severity risk is very high, so this study aimed to evaluate the nutritional and mineral contents of the green pods as a potential source for livestock feeds and soil fertilizer because, as far as we know, there is no use for this species. The seven different species of Acacia (Acacia mearnsii Link, Acacia longifolia (Andrews) Willd, Acacia melanoxylon R. Br., Acacia pycnantha Bentham, Acacia dealbata Link., Acacia retinodes Schlecht, and Acacia cyclops A. Cunn. ex G. Don fil) were evaluated. The results showed that Acacia green pods have a high protein, fibre and minerals content, especially in potassium (K), calcium (Ca) and magnesium (Mg). All species present a different profile of the studied parameters, suggesting different potentials for their future use. Near-infrared spectroscopy was a potential tool to predict the earlier quality of the Acacia green pods to better select the raw material for the different applications.

Effects of fine grinding on mid-infrared spectroscopic analysis of plant leaf nutrient content

Fourier transform mid infrared (FT-MIR) spectroscopy combined with modeling techniques has been studied as a useful tool for multivariate chemical analysis in agricultural research. A drawback of this method is the sample preparation requirement, in which samples must be dried and fine ground for accurate model calibrations. For research involving large sample sets, this may dramatically increase the time and cost of analysis. This study investigates the effect of fine grinding on model performance using leaf tissue from a variety of crop species. Dried leaf samples (N = 300) from various environmental conditions were obtained with data on 11 nutrients measured using chemical methods. The samples were scanned with attenuated total reflectance (ATR) and diffuse reflectance (DRIFT) FT-MIR techniques. Scanning was repeated after fine grinding for 2, 5, and 10 min. The spectra were analyzed for the 11 nutrients using partial least squares regression with a 75%/25% split for calibration and validation and repeated for 50 iterations. All analytes except for boron, iron, and zinc were well-modeled (average R² > 0.7), with higher R² values on ATR spectra. The 5 min level of fine grinding was found to be most optimal considering overall model performance and sample preparation time.

Bactericidal activity of Ag nanoparticles biosynthesized from Capsicum annuum pericarps against phytopathogenic Clavibacter michiganensis

Metallic nanoparticles of different compositions have already found numerous applications in various branches of industry, agriculture, and medicine. Given the well-known antibacterial activity of Ag, silver nanoparticles (AgNPs) are constantly being investigated for their promising ability to fight antibiotic-resistant pathogens. A promising candidate for AgNPs biosynthesis is chili pepper Capsicum annuum, cultivated worldwide and known for accumulating significant amounts of active substances. Phytochemical screening of aqueous extract of C. annuum pericarps demonstrated accumulation of 4.38 mg/g DW of total capsaicinoids, 14.56 mg GAE/g DW of total phenolic compounds, 1.67 mg QE/g DW of total flavonoids, and 1.03 mg CAE/g DW of total phenolic acids. All determined aromatic compounds carry various active functional groups, which effectively participate in the biosynthesis of AgNPs and are characterized by high antioxidant potential. Therefore, the present research focused on the facile, quick, and effective procedure for the biosynthesis of AgNPs, which were analyzed for their morphology such as shape and size through UV-visible, Fourier-transform infrared spectroscopy (FTIR) assays, and scanning electron microscopy. We found that the AgNPs biosynthesis resulted in changes in FTIR spectra, depicting the rearrangement of numerous functional groups, while the nanoparticles themselves were shown to be stable, spherical, 10-17 nm in size. Also we investigated the antibacterial properties of biosynthesized AgNPs, obtained with C. annuum fruit extracts, against a common phytopathogen Clavibacter michiganensis subsp. michiganensis. As was shown by zone inhibition assay, AgNPs showed dose-dependent 5.13-6.44 cm antibacterial activity, greatly exceeding the 4.98 cm inhibition area, produced by the precursor salt, AgNO₃.

The Potential Use of Near- and Mid-Infrared Spectroscopy in Kidney Diseases

Traditional renal biomarkers such as serum creatinine and albuminuria/proteinuria are rather insensitive since they change later in the course of the disease. In order to determine the extent and type of kidney injury, as well as to administer the proper therapy and enhance patient management, new techniques for the detection of deterioration of the kidney function are urgently needed. Infrared spectroscopy is a label-free and non-destructive technique having the potential to be a vital tool for quick and inexpensive routine clinical diagnosis of kidney disorders. The aim of this review is to provide an overview of near- and mid-infrared spectroscopy applications in patients with acute kidney injury and chronic kidney disease (e.g., diabetic nephropathy and glomerulonephritis).

Application of Spectroscopy Techniques for Monitoring (Bio)Catalytic Processes in Continuously Operated Microreactor Systems

In the last twenty years, the application of microreactors in chemical and biochemical industrial processes has increased significantly. The use of microreactor systems ensures efficient process intensification due to the excellent heat and mass transfer within the microchannels. Monitoring the concentrations in the microchannels is critical for a better understanding of the physical and chemical processes occurring in micromixers and microreactors. Therefore, there is a growing interest in performing in-line and on-line analyses of chemical and/or biochemical processes. This creates tremendous opportunities for the incorporation of spectroscopic detection techniques into production and processing lines in various industries. In this work, an overview of current applications of ultraviolet–visible, infrared, Raman spectroscopy, NMR, MALDI-TOF-MS, and ESI-MS for monitoring (bio)catalytic processes in continuously operated microreactor systems is presented. The manuscript includes a description of the advantages and disadvantages of the analytical methods listed, with particular emphasis on the chemometric methods used for spectroscopic data analysis.

Identification of ultrastructural and biochemical cuticular markers influencing temperature of ice nucleation in selected genotypes of corn

Corn is an economically important yet frost-sensitive crop, injured at the moment of ice nucleation. However, the influence of autumn temperatures on subsequent ice nucleation temperature is unknown. A 10-day chilling treatment under phytotron conditions ("mild", 18/6°C) or ("extreme", 10/5°C) generated no-visible damage but induced changes in the cuticle of the four genotypes in this study. The putatively more cold hardy Genotypes 884 and 959 leaves nucleated at colder temperatures compared to the more sensitive Genotypes 675 and 275. After chilling treatment, all four genotypes displayed warmer ice nucleation temperatures, with Genotype 884 expressing the largest shift to warmer nucleation temperatures. Cuticular hydrophobicity reduced while cuticular thickness remained unchanged under the chilling treatment. By contrast, under five-week field conditions, cuticle thickness increased in all genotypes, with Genotype 256 expressing a significantly thinner cuticle. FTIR spectroscopy revealed increases in the spectral regions of cuticular lipids in all genotypes after phytotron chilling treatment, while those spectral regions decreased under field conditions. A total of 142 molecular compounds were detected, with 28 compounds significantly induced under either phytotron or field conditions. Of these, seven compounds were induced under both conditions (Alkanes C31-C33, Ester C44, C46, β-amyrin, and triterpene). While clear differential responses were observed, chilling conditions preceding a frost modified physical and biochemical properties of the leaf cuticle under both phytotron and field conditions indicating this response is dynamic and could be a factor in selecting corn genotypes better adapted to avoiding frost with lower ice nucleation temperature.

In-line monitoring of protein concentration with MIR spectroscopy during UFDF

Rapid increase of product titers in upstream processes has presented challenges for downstream processing, where purification costs increase linearly with the increase of the product yield. Hence, innovative solutions are becoming increasingly popular. Process Analytical Technology (PAT) tools, such as spectroscopic techniques, are on the rise due to their capacity to provide real-time, precise analytics. This ensures consistent product quality and increased process understanding, as well as process control. Mid-infrared spectroscopy (MIR) has emerged as a highly promising technique within recent years, owing to its ability to monitor several critical process parameters at the same time and unchallenging spectral analysis and data interpretation. For in-line monitoring, Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy (ATR-FTIR) is a method of choice, as it enables reliable measurements in a liquid environment, even though water absorption bands are present in the region of interest. Here, we present MIR spectroscopy as a monitoring tool of critical process parameters in ultrafiltration/diafiltration (UFDF). MIR spectrometer was integrated in the UFDF process in an in-line fashion through a single-use flow cell containing a single bounce silicon ATR crystal. The results indicate that the one-point calibration algorithm applied to the MIR spectra, predicts highly accurate protein concentrations, as compared with validated offline analytical methods.