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

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.

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.

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.

Full-field optical spectroscopy at a high spectral resolution using atomic vapors

Spectral imaging techniques extract spectral information using dispersive elements in combination with optical microscopes. For rapid acquisition, multiplexing spectral information along one dimension of imaged pixels has been demonstrated in hyperspectral imaging, as well as in Raman and Brillouin imaging. Full-field spectroscopy, i.e., multiplexing where imaged pixels are collected in 2D simultaneously while spectral analysis is performed sequentially, can increase spectral imaging speed, but so far has been attained at low spectral resolutions. Here, we extend 2D multiplexing to high spectral resolutions of ∼80 MHz (∼0.0001 nm) using high-throughput spectral discrimination based on atomic transitions.

Visible- and near-infrared hyperspectral imaging for the quantitative analysis of PD-L1+ cells in human lymphomas: Comparison with fluorescent multiplex immunohistochemistry

INTRODUCTION

We analyzed the expression of PD-L1 in human lymphomas using hyperspectral imaging (HSI) compared to visual assessment (VA) and conventional digital image analysis (DIA) to strengthen further the value of HSI as a tool for the evaluation of brightfield-based immunohistochemistry (IHC). In addition, fluorescent multiplex immunohistochemistry (mIHC) was used as a second detection method to analyze the impact of a different detection method.

MATERIAL AND METHODS

18 cases (6 follicular lymphomas and 12 diffuse large B-cell lymphomas) were stained for PD-L1 by IHC and for PD-L1, CD3, and CD8 by fluorescent mIHC. The percentage of positively stained cells was evaluated with VA, HSI, and DIA for IHC and VA and DIA for mIHC. Results were compared between the different methods of detection and analysis.

RESULTS

An overall high concordance was found between VA, HSI, and DIA in IHC (Cohens Kappa = 0.810^VA/HSI, 0.710 ^VA/DIA, and 0.516 ^HSI/DIA) and for VA^mIHCversus DIA^mIHC (Cohens Kappa = 0.894). Comparing IHC and mIHC general agreement differed depending on the methods compared but reached at most a moderate agreement (Coheńs Kappa between 0.250 and 0.483). This is reflected by the significantly higher percentage of PD-L1+ cells found with mIHC (p^Friedman = 0.014).

CONCLUSION

Our study shows a good concordance for the different analysis methods. Compared to VA and DIA, HSI proved to be a reliable tool for assessing IHC. Understanding the regulation of PD-L1 expression will further enlighten the role of PD-L1 as a biomarker. Therefore it is necessary to develop an instrument, such as HSI, which can offer a reliable and objective evaluation of PD-L1 expression.

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.

The Convergence of FTIR and EVs: Emergence Strategy for Non-Invasive Cancer Markers Discovery

In conjunction with imaging analysis, pathology-based assessments of biopsied tissue are the gold standard for diagnosing solid tumors. However, the disadvantages of tissue biopsies, such as being invasive, time-consuming, and labor-intensive, have urged the development of an alternate method, liquid biopsy, that involves sampling and clinical assessment of various bodily fluids for cancer diagnosis. Meanwhile, extracellular vesicles (EVs) are circulating biomarkers that carry molecular profiles of their cell or tissue origins and have emerged as one of the most promising biomarkers for cancer. Owing to the biological information that can be obtained through EVs' membrane surface markers and their cargo loaded with biomolecules such as nucleic acids, proteins, and lipids, EVs have become useful in cancer diagnosis and therapeutic applications. Fourier-transform infrared spectroscopy (FTIR) allows rapid, non-destructive, label-free molecular profiling of EVs with minimal sample preparation. Since the heterogeneity of EV subpopulations may result in complicated FTIR spectra that are highly diverse, computational-assisted FTIR spectroscopy is employed in many studies to provide fingerprint spectra of malignant and non-malignant samples, allowing classification with high accuracy, specificity, and sensitivity. In view of this, FTIR-EV approach carries a great potential in cancer detection. The progression of FTIR-based biomarker identification in EV research, the rationale of the integration of a computationally assisted approach, along with the challenges of clinical translation are the focus of this review.

Classification and Prediction by Pigment Content in Lettuce (Lactuca sativa L.) Varieties Using Machine Learning and ATR-FTIR Spectroscopy

Green or purple lettuce varieties produce many secondary metabolites, such as chlorophylls, carotenoids, anthocyanins, flavonoids, and phenolic compounds, which is an emergent search in the field of biomolecule research. The main objective of this study was to use multivariate and machine learning algorithms on Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR)-based spectra to classify, predict, and categorize chemometric attributes. The cluster heatmap showed the highest efficiency in grouping similar lettuce varieties based on pigment profiles. The relationship among pigments was more significant than the absolute contents. Other results allow classification based on ATR-FTIR fingerprints of inflections associated with structural and chemical components present in lettuce, obtaining high accuracy and precision (>97%) by using principal component analysis and discriminant analysis (PCA-LDA)-associated linear LDA and SVM machine learning algorithms. In addition, PLSR models were capable of predicting Chla, Chlb, Chla+b, Car, AnC, Flv, and Phe contents, with R2P and RPDP values considered very good (0.81−0.88) for Car, Anc, and Flv and excellent (0.91−0.93) for Phe. According to the RPDP metric, the models were considered excellent (>2.10) for all variables estimated. Thus, this research shows the potential of machine learning solutions for ATR-FTIR spectroscopy analysis to classify, estimate, and characterize the biomolecules associated with secondary metabolites in lettuce.

Use of ATR-FTIR spectroscopy for analysis of water deficit tolerance in Physalis peruviana L

Treatments that allow plants to better tolerate water deficit become essential, such as the application of chemical priming. In addition, it is essential to use analyses capable of measuring these effects at the biomolecular level, complementing the other physiological evaluations. In view of the above, this study aimed to evaluate the use of attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy for analyses of water deficit tolerance in Physalis peruviana plants. For this, samples of leaves, stems and roots of plants subjected to different pretreatments with proline (10 mM and 20 mM), sodium nitroprusside (SNP 25 μM and 50 μM) and H₂O as control, aiming at increasing tolerance to water deficit, were evaluated. The chemical agents used attenuated water deficit in P. peruviana plants, influencing phenotypic characterization and spectral analyses. Analysis of FTIR spectra indicates that different functional groups present in leaves, stems and roots were influenced by water deficit and priming treatments. Changes in lipid levels contributed to reducing water losses by increasing the thickness of cuticular wax. Accumulation of proteins and carbohydrates promoted osmoregulation and maintenance of the water status of plants. Thus, water deficit causes changes in the functional groups present in the organs of P. peruviana, and the ATR-FTIR technique is able to detect these biomolecular changes, helping in the selection of priming treatments to increase tolerance to water deficit.

The mechanisms and prediction of non-structural carbohydrates accretion and depletion after mechanical wounding in slash pine (Pinus elliottii) using near-infrared reflectance spectroscopy

BACKGROUND

The allocation of non-structural carbohydrates (NSCs) plays a critical role in the physiology and metabolism of tree growth and survival defense. However, little is known about the allocation of NSC after continuous mechanical wounding of pine by resin tapping during tree growth.

RESULTS

Here, we examine the NSC allocation in plant tissues after 3 year lasting resin tapping, and also investigate the use of near-infrared reflectance (NIR) spectroscopy to quantify the NSC, starch and free sugar (e.g., sucrose, glucose, and fructose) concentrations in different plant tissues of slash pine. Spectral measurements on pine needle, branch, trunk phloem, and root were obtained before starch and free sugar concentrations were measured in the laboratory. The variation of NSC, starch and free sugars in different plant tissues after resin tapping was analyzed. Partial least squares regression was applied to calibrate prediction models, models were simulated 100 times for model performance and error estimation. More NSC, starch and free sugars were stored in winter than summer both in tapped and control trees. The position of resin tapping significantly influenced the NSCs allocation in plant tissues: more NSCs were transformed into free sugars for defensive resin synthesis close to the tapping wound rather than induced distal systemic responses. Models for predicting NSC and free sugars of plant tissues showed promising results for the whole tree for fructose (R²_CV = 0.72), glucose (R²_CV = 0.67), NSCs (R²_CV = 0.66) and starch (R²_CV = 0.58) estimates based on NIR models. Models for individual plant tissues also showed reasonable predictive ability: the best model for NSCs and starch prediction was found in root. The significance multivariate correlation algorithm for variable selection significantly reduced the number of variables. Important variables were identified, including features at 1021-1290 nm, 1480, 1748, 1941, 2020, 2123 and 2355 nm, which are highly related to NSC, starch, fructose, glucose and sucrose.

CONCLUSIONS

NIR spectroscopy provided a rapid and cost-effective method to monitor NSC, starch and free sugar concentrations after continuous resin tapping. It can be used for studying the trade-off between growth and production of defensive metabolites.

Dielectric dispersion characteristics of the phospholipid bilayer with subnanometer resolution from terahertz to mid-infrared

There is growing interest in whether the myelinated nerve fiber acts as a dielectric waveguide to propagate terahertz to mid-infrared electromagnetic waves, which are presumed stable signal carrier for neurotransmission. The myelin sheath is formed as a multilamellar biomembrane structure, hence insights into the dielectric properties of the phospholipid bilayer is essential for a complete understanding of the myelinated fiber functioning. In this work, by means of atomistic molecular dynamics simulations of the dimyristoylphosphatidylcholine (DMPC) bilayer in water and numerical calculations of carefully layered molecules along with calibration of optical dielectric constants, we for the first time demonstrate the spatially resolved (in sub-nm) dielectric spectrum of the phospholipid bilayer in a remarkably wide range from terahertz to mid-infrared. More specifically, the membrane head regions exhibit both larger real and imaginary permittivities than that of the tail counterparts in the majority of the 1–100 THz band. In addition, the spatial variation of dielectric properties suggests advantageous propagation characteristics of the phospholipid bilayer in a relatively wide band of 55–85 THz, where the electromagnetic waves are well confined within the head regions.

Near infrared spectroscopy (NIRS) coupled with chemometric methods to identify and estimate taxonomic relationships of flies with forensic potential (Diptera: Calliphoridae and Sarcophagidae)

Infrared spectroscopy has been gaining prominence in entomology, such as for solving taxonomic problems, sexing adult specimens, determining the age of immature specimens, detecting drugs of abuse in fly larvae, and can be an important technique in Forensic Entomology. In order to help identify the species of Calliphoridae and Sarcophagidae families, the present study aimed to evaluate the use of near infrared spectroscopy (NIRS) coupled with chemometric methods for separating fly specimens into taxonomic categories and understanding the taxonomic relationship between them. Spectra collected from nine species of flies were subjected to unsupervised principal component analysis (PCA) and hierarchical cluster analysis (HCA), in which we sought to visualize the relationship between the samples (segregation of genera and families) with subsequent identification. In PCA, the best model was achieved using five principal components (PCs), which explained 99.16% of total variance of the original data set. The first principal component (PC1) and the fourth principal component (PC4) provided the best segregation, the latter being more important in the segregation of the species Chrysomya albiceps, Lucilia eximia, and Ravinia belforti from the others. In the HCA dendrogram, there was a clear separation between the specimens by family (Calliphoridae and Sarcophagidae) and genera (Chrysomya, Lucilia, Oxysarcodexia, Peckia and Ravinia). This study shows that NIRS is efficient to identify flies' taxonomic properties, such as family and genera, providing quick evidence for the tested species identity.

Spectral Preprocessing Combined with Deep Transfer Learning to Evaluate Chlorophyll Content in Cotton Leaves

Rapid determination of chlorophyll content is significant for evaluating cotton's nutritional and physiological status. Hyperspectral technology equipped with multivariate analysis methods has been widely used for chlorophyll content detection. However, the model developed on one batch or variety cannot produce the same effect for another due to variations, such as samples and measurement conditions. Considering that it is costly to establish models for each batch or variety, the feasibility of using spectral preprocessing combined with deep transfer learning for model transfer was explored. Seven different spectral preprocessing methods were discussed, and a self-designed convolutional neural network (CNN) was developed to build models and conduct transfer tasks by fine-tuning. The approach combined first-derivative (FD) and standard normal variate transformation (SNV) was chosen as the best pretreatment. For the dataset of the target domain, fine-tuned CNN based on spectra processed by FD + SNV outperformed conventional partial least squares (PLS) and squares-support vector machine regression (SVR). Although the performance of fine-tuned CNN with a smaller dataset was slightly lower, it was still better than conventional models and achieved satisfactory results. Ensemble preprocessing combined with deep transfer learning could be an effective approach to estimate the chlorophyll content between different cotton varieties, offering a new possibility for evaluating the nutritional status of cotton in the field.

Infrared spectroscopy (NIRS and ATR-FTIR) together with multivariate classification for non-destructive differentiation between female mosquitoes of Aedes aegypti recently infected with dengue vs. uninfected females

One of the most important steps in preventing arboviruses is entomological surveillance. The main entomological surveillance action is to detect vector foci in the shortest possible stages. In this work, near and medium infrared spectra collected from female Aedes aegypti mosquitoes recently infected and not infected with dengue were used in order to build chemometric models capable of differentiating the spectra of each class. For this, computational algorithms such as Successive Projection Algorithm (SPA) and Genetic Algorithm (GA) were used together with Linear Discriminant Analysis (LDA). The constructed models were evaluated with sensitivity and specificity calculations. It was observed that models based on near infrared (NIR) spectra have better classification results when compared to mid infrared (MIR) spectra, as well as models based on GA present better results when compared to those based on SPA. Thus, NIR-GA-LDA obtained the best results, reaching 100.00 % for sensitivity and specificity. NIR spectroscopy is 18 times faster and 116 times cheaper than RT-qPCR. The findings reported in this study may have important applications in the field of entomological surveillance, prevention and control of dengue vectors. In the future, mosquito traps equipped with portable NIR instruments capable of detecting infected mosquitoes may be used, in order to enable an action plan to prevent future outbreaks of the disease.

Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits

BACKGROUND

Characterizing plant genetic resources and their response to the environment through accurate measurement of relevant traits is crucial to genetics and breeding. Spatial organization of the maize ear provides insights into the response of grain yield to environmental conditions. Current automated methods for phenotyping the maize ear do not capture these spatial features.

RESULTS

We developed EARBOX, a low-cost, open-source system for automated phenotyping of maize ears. EARBOX integrates open-source technologies for both software and hardware that facilitate its deployment and improvement for specific research questions. The imaging platform consists of a customized box in which ears are repeatedly imaged as they rotate via motorized rollers. With deep learning based on convolutional neural networks, the image analysis algorithm uses a two-step procedure: ear-specific grain masks are first created and subsequently used to extract a range of trait data per ear, including ear shape and dimensions, the number of grains and their spatial organisation, and the distribution of grain dimensions along the ear. The reliability of each trait was validated against ground-truth data from manual measurements. Moreover, EARBOX derives novel traits, inaccessible through conventional methods, especially the distribution of grain dimensions along grain cohorts, relevant for ear morphogenesis, and the distribution of abortion frequency along the ear, relevant for plant response to stress, especially soil water deficit.

CONCLUSIONS

The proposed system provides robust and accurate measurements of maize ear traits including spatial features. Future developments include grain type and colour categorisation. This method opens avenues for high-throughput genetic or functional studies in the context of plant adaptation to a changing environment.

Alleviating excess boron stress in tomato calli by applying benzoic acid to various biochemical strategies

Benzoic acid (BA) represents vital roles in plant activity and response to diverse unfavorable conditions. However, its participation in mitigating excess boron (EB) stress in plants is elusive. Herein, we have examined the impacts of BA (1 μM) in controlling boron (B) uptake in tomato (Solanum lycopersicum L.) calli exposed to various EB levels (0, 1, 2, and 3 mM). The free, semi-bound, and bound B forms were stimulated by EB, while these forms were reduced in B-stressed calli by BA supplementation (40.37%, 36.08%, and 66.91%, respectively, less than 3 mM B-stressed calli alone). EB caused a reduction in the uptake of potassium (K⁺), calcium (Ca²⁺), magnesium (Mg²⁺), and nitrite (NO₂^-) while increasing the concentration of phosphorus (P), nitrate (NO₃^-), sulfur (S), and sulfate (SO₄^2-) in B-stressed calli. BA application induced the uptake of K⁺, Ca²⁺, Mg²⁺, NO₃^-, S, and SO₄^2-; however, it reduced P and NO₂^- concentrations in B-stressed calli. EB reduced nitrate reductase activity (NR), while BA application did not alleviate this reduction. EB treatments significantly, in most cases, increased sulfite oxidase (SO) activity. Supplementation of BA along with EB further enhanced SO activity. Cell wall components (cellulose, hemicellulose, and pectin) were decreased under EB treatments but considerably increased in B-stressed calli by BA application. Fourier Transform Infrared Spectrometer (FT-IR) output showed that EB treatments with/without BA led to alterations in cell wall functional groups of calli. Our findings indicated that BA application enabled tomato callus to counteract the harmful effect of EB, leading to improved callus growth.

Comparative phytochemistry, antioxidant, antidiabetic, and anti-inflammatory activities of traditionally used Ocimum basilicum L. Ocimum gratissimum L., and Ocimum tenuiflorum L

Ocimum spp. are the widely studied herbal plants because of their diverse biological activities. The present study aimed at comparative extraction of secondary metabolites and evaluation of their biological activities in different solvents such as acetone, ethanol, methanol, and water. Three Ocimum species, namely Ocimum basilicum L. (Green tulsi), Ocimum gratissimum L. (Jungli tulsi), and Ocimum tenuiflorum (Black tulsi), were selected for this study. Leaf extracts from dried powder of these species were prepared in different solvents. The contents of total phenolics, flavonoids, and total condensed tannins were estimated using standard assays. Fingerprint analysis using UV, Fourier transform infrared (FT-IR), and fluorescent spectroscopy was also conducted. Total antioxidant, antidiabetic, and anti-inflammatory activities of the extracts were evaluated. Fingerprint analysis indicated the presence of a sufficient level of polyphenolics in all the solvent extracts. Among all the solvents, acetone provided a higher yield of phenolics, flavonoids, and tannins in all Ocimum species. Black Ocimum showed the maximum level of antioxidants. All Ocimum extracts exhibited a sufficient level of antidiabetic and anti-inflammatory activities. The results indicated that by using appropriate solvents, bioactive compounds from Ocimum species can be extracted and used as therapeutic agents with potential biological activities.

Multispectral Optical Confusion System: Visible to Infrared Coloration with Fractal Nanostructures

Optical confusion refers to a camouflage technique assimilated with the surroundings through manipulating colors and patterns. With the advances in multispectral imagery detection systems, multispectral camouflage studies on simultaneous deceptions in the visible to infrared ranges remain a key challenge. Thus, creating pixelated patterns is essential for mimicking background signatures by assimilating both colors and patterns. In this study, a multispectral optical confusion system (MOCS) comprising pixelated silicon-based fractal nanostructures (Si-FNSs) is introduced to realize multispectral optical confusion. We analyzed the fractality of the Si-FNSs to understand the relationships between structural characteristics and optical properties with the aggregation phenomenon. The aggregation phenomenon changes the morphological heterogeneity by up to 38.5%, enabling a controllable range of visible reflectivity from 0.01 to 0.12 and infrared emissivity from 0.33 to 0.90. Visible and infrared colors were obtained by controlling the wet-etching time from 10 to 240 min and temperature from 40 to 100 °C. Finally, the MOCS consisting of pixelated Si-FNSs was designed and created by extracting the pattern from the simultaneously captured visible and infrared background images. Using the artificial backgrounds representing these images, we evaluated and compared the multispectral optical confusion performance of the MOCS with conventional camouflage surfaces.

Advances in cost-effective integrated spectrometers

The proliferation of Internet-of-Things has promoted a wide variety of emerging applications that require compact, lightweight, and low-cost optical spectrometers. While substantial progresses have been made in the miniaturization of spectrometers, most of them are with a major focus on the technical side but tend to feature a lower technology readiness level for manufacturability. More importantly, in spite of the advancement in miniaturized spectrometers, their performance and the metrics of real-life applications have seldomly been connected but are highly important. This review paper shows the market trend for chip-scale spectrometers and analyzes the key metrics that are required to adopt miniaturized spectrometers in real-life applications. Recent progress addressing the challenges of miniaturization of spectrometers is summarized, paying a special attention to the CMOS-compatible fabrication platform that shows a clear pathway to massive production. Insights for ways forward are also presented.

Label-free analytic histology of carotid atherosclerosis by mid-infrared optoacoustic microscopy

BACKGROUND AND AIMS

Analysis of atherosclerotic plaque composition is a vital tool for unraveling the pathological metabolic processes that contribute to plaque growth.

METHODS

We visualize the constitution of human carotid plaques by mid-infrared optoacoustic microscopy (MiROM), a method for label-free analytic histology that requires minimal tissue preparation, rapidly yielding large field-of-view en-face images with a resolution of a few micrometers. We imaged endarterectomy specimens (n = 3, 12 sections total) at specific vibrational modes, targeting carbohydrates, lipids and proteins. Additionally, we recorded spectra at selected tissue locations. We identified correlations in the variability in this high-dimensional data set using non-negative matrix factorization (NMF).

RESULTS

We visualized high-risk plaque features with molecular assignment. Consistent NMF components relate to different dominant tissue constituents, dominated by lipids, proteins, and cholesterol and carbohydrates respectively.

CONCLUSIONS

These results introduce MiROM as an innovative, stain-free, analytic histology technology for the biochemical characterization of complex human vascular pathology.

Towards Mapping Mouse Metabolic Tissue Atlas by Mid-Infrared Imaging with Heavy Water Labeling

Understanding metabolism is of great significance to decipher various physiological and pathogenic processes. While great progress has been made to profile gene expression, how to capture organ-, tissue-, and cell-type-specific metabolic profile (i.e., metabolic tissue atlas) in complex mammalian systems is lagging behind, largely owing to the lack of metabolic imaging tools with high resolution and high throughput. Here, the authors applied mid-infrared imaging coupled with heavy water (D₂ O) metabolic labeling to a scope of mouse organs and tissues. The premise is that, as D₂ O participates in the biosynthesis of various macromolecules, the resulting broad C-D vibrational spectrum should interrogate a wide range of metabolic pathways. Applying multivariate analysis to the C-D spectrum, the authors successfully identified both inter-organ and intra-tissue metabolic signatures of mice. A large-scale metabolic atlas map between different organs from the same mice is thus generated. Moreover, leveraging the power of unsupervised clustering methods, spatially-resolved metabolic signatures of brain tissues are discovered, revealing tissue and cell-type specific metabolic profile in situ. As a demonstration of this technique, the authors captured metabolic changes during brain development and characterized intratumoral metabolic heterogeneity of glioblastoma. Altogether, the integrated platform paves a way to map the metabolic tissue atlas for complex mammalian systems.

Research on Identification Technology of Field Pests with Protective Color Characteristics

Accurate identification of field pests has crucial decision-making significance for integrated pest control. Most current research focuses on the identification of pests on the sticky card or the case of great differences between the target and the background. There is little research on field pest identification with protective color characteristics. Aiming at the problem that it is difficult to identify pests with protective color characteristics in the complex field environment, a field pest identification method based on near-infrared imaging technology and YOLOv5 is proposed in this paper. Firstly, an appropriate infrared filter and ring light source have been selected to build an image acquisition system according to the wavelength with the largest spectral reflectance difference between the spectral curves of the pest (Pieris rapae) and its host plants (cabbage), which are formed by specific spectral characteristics. Then, field pest images have been collected to construct a data set, which has been trained and tested through YOLOv5. Experimental results demonstrate that the average time required to detect one pest image is 0.56 s, and the mAP reaches 99.7%.

Assessing Near-Infrared Spectroscopy (NIRS) for Evaluation of Aedes aegypti Population Age Structure

Given that older Aedes aegypti (L.) mosquitoes typically pose the greatest risk of pathogen transmission, the capacity to age grade wild Ae. aegypti mosquito populations would be a valuable tool in monitoring the potential risk of arboviral transmission. Here, we compared the effectiveness of near-infrared spectroscopy (NIRS) to age grade field-collected Ae. aegypti with two alternative techniques—parity analysis and transcript abundance of the age-associated gene SCP1. Using lab-reared mosquitoes of known ages from three distinct populations maintained as adults under laboratory or semi-field conditions, we developed and validated four NIRS models for predicting the age of field-collected Ae. aegypti. To assess the accuracy of these models, female Ae. aegypti mosquitoes were collected from Maricopa County, AZ, during the 2017 and 2018 monsoon season, and a subset were age graded using the three different age-grading techniques. For both years, each of the four NIRS models consistently graded parous mosquitoes as significantly older than nulliparous mosquitoes. Furthermore, a significant positive linear association occurred between SCP1 and NIRS age predictions in seven of the eight year/model combinations, although considerable variation in the predicted age of individual mosquitoes was observed. Our results suggest that although the NIRS models were not adequate in determining the age of individual field-collected mosquitoes, they have the potential to quickly and cost effectively track changes in the age structure of Ae. aegypti populations across locations and over time.

Leaf microscopy applications in photosynthesis research: identifying the gaps

Leaf imaging via microscopy has provided critical insights into research on photosynthesis at multiple junctures, from the early understanding of the role of stomata, through elucidating C4 photosynthesis via Kranz anatomy and chloroplast arrangement in single cells, to detailed explorations of diffusion pathways and light utilization gradients within leaves. In recent decades, the original two-dimensional (2D) explorations have begun to be visualized in three-dimensional (3D) space, revising our understanding of structure-function relationships between internal leaf anatomy and photosynthesis. In particular, advancing new technologies and analyses are providing fresh insight into the relationship between leaf cellular components and improving the ability to model net carbon fixation, water use efficiency, and metabolite turnover rate in leaves. While ground-breaking developments in imaging tools and techniques have expanded our knowledge of leaf 3D structure via high-resolution 3D and time-series images, there is a growing need for more in vivo imaging as well as metabolite imaging. However, these advances necessitate further improvement in microscopy sciences to overcome the unique challenges a green leaf poses. In this review, we discuss the available tools, techniques, challenges, and gaps for efficient in vivo leaf 3D imaging, as well as innovations to overcome these difficulties.

MEDUSA: An open-source and webcam based multispectral imaging system

Multispectral imaging is at the forefront of contactless surface analysis. Standard multispectral imaging systems use sophisticated software, cameras and light filtering optics. This paper discloses the building of a customizable and cost-effective multispectral imaging and analysis system. It integrates a web camera, light emitting diodes (LEDs) lighting, a semisphere for even lightening, an open-source Arduino™ development board and a free Python application to automatically obtain and visually analyze multispectral images. The device is hereafter called MEDUSA and its optical performance was tested for repeated Imaging consistency, visible and near infrared band sensitivity and lighting evenness. Four proof of concept tests were run in order to understand the advantageous use of this system, as compared to a simple visual score of diverse samples. Each of three qualitative tests used sets of 12 LED band spectral images to analyze ink changes in a counterfeit bill, surface bruises on Hass avocado fruits and transient changes in petri dish grown bacterial colonies. A fourth test used single band imaging in a set of standard laboratory analyzed plant samples, to quantitatively relate a red band light reflectance to its nitrogen content. These tests indicate that MEDUSA made images may yield qualitative and quantitative spectral information unseen to the naked eye, suggesting potential use in currency counterfeit tests, food quality analyses, microbial phenotyping and agricultural plant chemistry. MEDUSA can be freely reproduced and customized from this research, making it a powerful and affordable analytical tool to analyze a wide range of subtle chemical properties in samples at industrial and science fields.