Rapid detection of volatile compounds in apple wines using FT-NIR spectroscopy

Fourier transformed near-infrared combined with chemometric analysis: Sustainable quantification of natural laxatives in Cassia plants

FT-NIR and chemometrics are vital analytical tools for medicinal plant quality control. This study aimed to establish a rapid technique for quantifying sennoside A and B in Cassia plants (i.e., leaves, pods, and flowers) using Fourier Transformed Near-infrared Spectroscopy (FT-NIR). The calibration models were established by partial least squares (PLS), GA-PLS (Genetic Algorithm PLS), Bi-PLS (Backward Interval PLS), and Si-PLS (Synergy Interval PLS). Sennosides contents in Cassia were quantified using high-performance liquid chromatography. The outcomes indicated that GA-PLS and Si-PLS had the highest correlation coefficient of prediction (R2p) and lowest root-mean-square error of prediction (RMSEP). Then, the best prediction ability was achieved by Si-PLS (SA: R2p = 0.9732, RMSEP = 0.9720, 4400.761-4597.464 cm-1, 5203.003-5399.707 cm-1, and 5804.685-6001.388 cm-1; SB: R2p = 0.9924, RMSEP = 0.1220, 4547.3224-5091.152 cm-1, 5642.693-6186.521 cm-1 and 7285.747-7825.718 cm-1. The external validation of Si-PLS showed excellent results (R2p > 0.95), offering valuable theoretical guidance for developing an intelligent system to evaluate the quality of Cassia species in real-time.

Realize short-wave infrared luminescence in NaScP2O7:Cr3+,Yb3+ phosphor: Spectral and energy transfer

Short-wave infrared emitting phosphors have extensive applications for spectroscopy technology. The near-infrared phosphor NaScP2O7:Cr3+ that we present in this work has a full width at half maximum (FWHM) of approximately 196 nm, which ranges from 700 to 1200 nm. To achieve efficient short-wave infrared, Yb3+ ions were co-doped. The NaScP2O7:Cr3+,Yb3+ material emitted infrared bands with peaks at 970 and 1003 nm upon excitation at450 nm. Benefitting from energy transfer (ET), the light in the 900-1200 nm from Yb3+ is effectively enhanced. Photoluminescence spectra, thermal quenching, and decay curves of Cr3+/Yb3+ single and codoped NaScP2O7 were investigated. An internal quantum yield of 29.6 % wasdemonstrated by the optimized phosphor NaScP2O7:Cr3+,Yb3+. Furthermore, The final fabrication of the short-wave infrared pc-LED was done through the combination of a blue-emitting chip and NaScP2O7:Cr3+,Yb3+ phosphor, thereby showing great promise for real implementations.

Near-Infrared Light Emitting Metal Halides: Materials, Mechanisms, and Applications

Near-Infrared (NIR) light emitting metal halides are emerging as a new generation of optical materials owing to their appealing features, which include low-cost synthesis, solution processability, and adjustable optical properties. NIR-emitting perovskite-based light-emitting diodes (LEDs) have reached an external quantum efficiency (EQE) of over 20% and a device stability of over 10,000 h. Such results have sparked an interest in exploring new NIR metal halide emitters. In this review, several different types of NIR-emitting metal halides, including lead/tin bromide/iodide perovskites, lanthanide ions doped/based metal halides, double perovskites, low dimensional hybrid and Bi3+/Sb3+/Cr3+ doped metal halides, are summarized, and their recent advancement is assessed. The characteristics and mechanisms of narrow-band or broadband NIR luminescence in all these materials are discussed in detail. Also, the various applications of NIR-emitting metal halides are highlighted and an outlook for the field is provided.

Achieving Luminescence of Sr3Ga1.98In0.02Ge4O14:0.03Cr3+ via [In3+] Substitution [Ga3+] and Its Application to NIR pc-LED in Non-Destructive Testing

Cr3+-doped Sr3Ga2Ge4O14:0.03Cr3+ (SGGO:0.03Cr3+) phosphor was synthesized via a high-temperature solid-phase method. Considering the tunable structure of SGGO, Ga3+ ions in the matrix were substituted with In3+ ions at a certain concentration. The tuned phosphor produced a red-shifted emission spectrum, with its luminescence intensity at 423 K maintained at 63% of that at room temperature; moreover, the internal quantum efficiency increased to 65.60%, and the external quantum efficiency correspondingly increased to 21.94%. On this basis, SGIGO:0.03Cr3+ was encapsulated into a pc-LED, which was applied in non-destructive testing (NDT) experiments, successfully realizing the recognition of water and anhydrous ethanol, proving its potential application in the field of NDT.

Improvement of the prediction of a visual apple ripeness index under seasonal variation by NIR spectral model correction

Apple ripeness assessment is essential to ensure its post-harvest commercial value, and the visible/near-infrared(NIR) spectral models that are effective in achieving this goal are prone to failure due to seasonal or instrumental factors. This study has proposed a visual ripeness index (VRPI) determined by parameters such as soluble solids, titratable acids, etc., which vary during the ripening period of the apple. The R and RMSE of the index prediction model based on the 2019 sample were 0.871 to 0.913 and 0.184 to 0.213 respectively. The model failed to predict the next two years of the sample, which was effectively addressed by model fusion and correction. For the 2020 and 2021 samples, the revised model improves R by 6.8% and 10.6% and reduces RMSE by 52.2% and 32.2% respectively. The results showed that the global model is adapted to the correction of the VRPI spectral prediction model under seasonal variation.

CNN-LSTM Neural Network for Identification of Pre-Cooked Pasta Products in Different Physical States Using Infrared Spectroscopy

Infrared (IR) spectroscopy is nondestructive, fast, and straightforward. Recently, a growing number of pasta companies have been using IR spectroscopy combined with chemometrics to quickly determine sample parameters. However, fewer models have used deep learning models to classify cooked wheat food products and even fewer have used deep learning models to classify Italian pasta. To solve these problems, an improved CNN-LSTM neural network is proposed to identify pasta in different physical states (frozen vs. thawed) using IR spectroscopy. A one-dimensional convolutional neural network (1D-CNN) and long short-term memory (LSTM) were constructed to extract the local abstraction and sequence position information from the spectra, respectively. The results showed that the accuracy of the CNN-LSTM model reached 100% after using principal component analysis (PCA) on the Italian pasta spectral data in the thawed state and 99.44% after using PCA on the Italian pasta spectral data in the frozen form, verifying that the method has high analytical accuracy and generalization. Therefore, the CNN-LSTM neural network combined with IR spectroscopy helps to identify different pasta products.

Classification of Black Mahlab seeds (Monechma ciliatum) using GC-MS and FT-NIR and simultaneous prediction of their major volatile compounds using chemometrics

The identification of geographical origin is an important factor in assessing the quality of aromatic and medicinal seeds such as Black Mahlab (Monechma ciliatum). However, at present, there are no studies concerning Black Mahlab Seeds (BMSs). To identify the geographical origin of BMSs, we have used gas chromatography-mass spectrometry (GC-MS) and Fourier transform infrared spectroscopy (FT-NIR) combined with chemometrics. Chemometrics analysis showed that FT-NIR and GC-MS can be used to discriminate the geographical origin of BMSs. FT-NIR coupled with the partial least squares regression (PLSR) was applied to develop the calibration models. The calibration models had a coefficient of determination (R_c²) of 0.82 for coumarin and 0.81 for methyl salicylate. The prediction model (R_p²) values ranged from 0.83 for coumarin to 0.77 for methyl salicylate. Overall, the chemometrics presented correct classification, and PLSR accurately predicted the volatiles, with an RMSEP range of 0.9 to 0.16 for the two volatiles targeted.

Analyzing the Quality Parameters of Apples by Spectroscopy from Vis/NIR to NIR Region: A Comprehensive Review

Spectroscopic methods deliver a valuable non-destructive analytical tool that provides simultaneous qualitative and quantitative characterization of various samples. Apples belong to the world's most consumed crops and with the current challenges of climate change and human impacts on the environment, maintaining high-quality apple production has become critical. This review comprehensively analyzes the application of spectroscopy in near-infrared (NIR) and visible (Vis) regions, which not only show particular potential in evaluating the quality parameters of apples but also in optimizing their production and supply routines. This includes the assessment of the external and internal characteristics such as color, size, shape, surface defects, soluble solids content (SSC), total titratable acidity (TA), firmness, starch pattern index (SPI), total dry matter concentration (DM), and nutritional value. The review also summarizes various techniques and approaches used in Vis/NIR studies of apples, such as authenticity, origin, identification, adulteration, and quality control. Optical sensors and associated methods offer a wide suite of solutions readily addressing the main needs of the industry in practical routines as well, e.g., efficient sorting and grading of apples based on sweetness and other quality parameters, facilitating quality control throughout the production and supply chain. This review also evaluates ongoing development trends in the application of handheld and portable instruments operating in the Vis/NIR and NIR spectral regions for apple quality control. The use of these technologies can enhance apple crop quality, maintain competitiveness, and meet the demands of consumers, making them a crucial topic in the apple industry. The focal point of this review is placed on the literature published in the last five years, with the exceptions of seminal works that have played a critical role in shaping the field or representative studies that highlight the progress made in specific areas.

Research Progress and Development of Near-Infrared Phosphors

Near-infrared (NIR) light has attracted considerable attention in diverse applications, such as food testing, security monitoring, and modern agriculture. Herein, the advanced applications of NIR light, as well as various devices to realize NIR light, have been described. Among the diverse NIR light source devices, the NIR phosphor-converted light-emitting diode (pc-LED), serving as a new-generation NIR light source, has obtained attention due to its wavelength-tunable behavior and low-cost. As one of the key materials of the NIR pc-LED, a series of NIR phosphors have been summarized depending on the type of luminescence center. Meanwhile, the characteristic transitions and luminescence properties of the above phosphors are illustrated in detail. In addition, the status quo of NIR pc-LEDs, as well as the potential problems and future developments of NIR phosphors and applications have also been discussed.

Measurement of nitrogen content in rice plant using near infrared spectroscopy combined with different PLS algorithms

Nitrogen plays an important role in rice growth, and determination of nitrogen content in rice plants is of great significance in assessing plant nutritional status and allowing precision cultivation. Traditional chemical methods for determining nitrogen content have the disadvantages of destructive sampling and lengthy analysis times. Here, the feasibility of rapid nitrogen content analysis by near-infrared (NIR) spectroscopy of rice plants was studied. Spectral data from 447 rice samples at several growth stages were used to establish a predictive model. Different spectral preprocessing methods and characteristic selection methods were compared, such as interval partial least-squares (iPLS), synergy interval partial least-squares (SiPLS), and moving-window partial least-squares (mwPLS). The SiPLS method exhibited better performance than mwPLS or iPLS. Specifically, the combination of four subintervals (7, 26, 27, and 28), with characteristic bands at 5299-4451 cm^-1 and 10445-10423 cm^-1, resulted in the best model. The optimal SiPLS model had a correlation coefficient of 0.9533 and a root mean square error of prediction (RMSEP) of 0.1952 on the prediction set. Compared to using the full spectra, using SiPLS reduced the number of characteristics by 87 % in the model, and RMSEP was reduced from 0.2284 to 0.1952. The results demonstrate that NIR spectroscopy combined with the SiPLS algorithm can be applied to quickly determine nitrogen content in rice plants. This study provides a technical framework to guide future precision agriculture efforts with respect to nitrogen application.

Nondestructive Testing of Pear Based on Fourier Near-Infrared Spectroscopy

Fourier transform near-infrared (FT-NIR) spectroscopy is a nondestructive, rapid, real-time analysis of technical detection methods with an important reference value for producers and consumers. In this study, the feasibility of using FT-NIR spectroscopy for the rapid quantitative analysis and qualitative analysis of ‘Zaosu’ and ‘Dangshansuli’ pears is explored. The quantitative model was established by partial least squares (PLS) regression combined with cross-validation based on the spectral data of 340 pear fresh fruits and synchronized with the reference values determined by conventional assays. Furthermore, NIR spectroscopy combined with cluster analysis was used to identify varieties of ‘Zaosu’ and ‘Dangshansuli’. As a result, the model developed using FT-NIR spectroscopy gave the best results for the prediction models of soluble solid content (SSC) and titratable acidity (TA) of ‘Dangshansuli’ (residual prediction deviation, RPD: 3.272 and 2.239), which were better than those developed for ‘Zaosu’ SSC and TA modeling (RPD: 1.407 and 1.471). The results also showed that the variety identification of ‘Zaosu’ and ‘Dangshansuli’ could be carried out based on FT-NIR spectroscopy, and the discrimination accuracy was 100%. Overall, FT-NIR spectroscopy is a good tool for rapid and nondestructive analysis of the internal quality and variety identification of fresh pears.

Age Discrimination of Chinese Baijiu Based on Midinfrared Spectroscopy and Chemometrics

Baijiu is a traditional and popular Chinese liquor which is affected by the storage time. The longer the storage time of Baijiu is, the better its quality is. In this paper, the raw and mellow Baijiu samples from different storage time are discriminated accurately throughout midinfrared (MIR) spectroscopy and chemometrics. Firstly, changing regularities of the substances in Chinese Baijiu are discussed by gas chromatography-mass spectrometry (GC-MS) during the aging process. Then, infrared spectrums of Baijiu samples are processed by smoothing, multivariate baseline correction, and the first and second derivative processing, but no significant variation can be observed. Next, the spectral date pretreatment methods are constructively introduced, and principal component analysis (PCA) and discriminant analysis (DA) are developed for data analyses. The results show that the accuracy rates of samples by the DA method in calibration and validation sets are 91.7% and 100%, respectively. Consequently, an identification model based on support vector machine (SVM) and PCA is established combined with the grid search strategy and cross-validation methods to discriminate the age of Chinese Baijiu validly, where 100% classification accuracy rate is obtained in both training and test sets.

D2h-Symmetric Tetratellurium Clusters in Silicate Glass as a Broadband NIR Light Source for Spectroscopy Applications

Broadband near-infrared (NIR) light sources present attractive opportunities for potential applications in high-capacity telecommunication, temperature sensing, energy conversion, and NIR spectroscopy. While significant effort has been spent on materials doped with rare-earth and transition-metal ions, the achievement of these materials with ultrabroadband NIR emission and desired wavelength region remains a long-standing challenge, especially operating in the spectral region between 700 and 1300 nm. Here, such emission is developed in tellurium (Te) cluster-doped silicate glass for the first time. Furthermore, the mechanism of the NIR luminescence due to D2h-symmetric tetratellurium (Te₄) clusters is identified by density functional theory (DFT) calculations. For intense luminescence, a model for the generation and stabilization of Te clusters by tailoring topological cages via adjustment of the Na₂O and Al₂O₃ contents and by optimizing the content of the dopant is proposed. Various stable Te clusters embedded into glass exhibit intense visible (Vis) to NIR broadband luminescence (400-1300 nm) with a spectral gap of 900 nm. In a demonstration experiment, a light-emitting diode (LED) device is fabricated from Te cluster-doped glass. This study opens a new opportunity for Te cluster-doped glass as a broadband NIR light source for spectroscopy applications.

Rapid detection of quality of Japanese fermented soy sauce using near-infrared spectroscopy

This study investigated the feasibility of rapidly evaluating the final quality of Japanese fermented soy sauce (shoyu) using NIR spectroscopy and partial least-squares (PLS) regression. In total, 110 shoyu samples that had been entered in the annual soy sauce competition from 2016 to 2018 were collected and analyzed. The transmittance spectra (400-1800 nm) and the transflectance spectra (680-2500 nm) of these samples were acquired and processed by different pre-treatments. PLS regression was applied to the raw and processed spectra to construct models based on a calibration set (76 shoyu samples from 2016 and 2017) and to evaluate these models using a validation set (34 shoyu samples from 2018), according to their values for bias and root mean square error of prediction (RMSEP). The results showed that the models constructed using the full spectra of transflectance performed better than those using transmittance spectra. Comparing the influence of different regions in the transflectance spectra enabled the accuracy of the models to be improved. The model constructed from transflectance spectra from the 1800 to 2500 nm region using pre-treatment of second derivative was superior to the other models, with a bias value of -2 and the lowest RMSEP value of 13 in the validation set. To further narrow the wavelength range, the models constructed using the spectral region from 2050 to 2400 nm also showed a better performance for predicting the sensory quality of soy sauce products. This study has demonstrated that the NIR spectroscopy technique could be used as an alternative routine quality control procedure, which can rapidly and economically classify the quality of soy sauce products.

Designing a super broadband near infrared material Mg3Y2Ge3O12:Cr3+ using cation inversion for future light sources

A series of broad emission band near infrared materials Mg₃Y₂Ge₃O₁₂:Cr³⁺ (650–1200 nm) was prepared based on cation inversion. For trivalent chromium ions (Cr³⁺), garnet structural components can provide conditions for the occurrence of cation inversion. With an increase in Cr³⁺ concentration, the Mg²⁺ and Ge⁴⁺ cations are inverted to ensure valence equilibrium, which was explained by recording the low temperature spectrum of the structure and carrying out structural refinement. As a result, this structure provides a new luminescent center [GeO₆] for Cr³⁺, leading to a secondary enhancement in emission intensity. The wavelength of the main peak was found to move from 771 to 811 nm, and the full width at half maximum (FWHM) was broadened from 180 to 226 nm. The lattice occupation, luminescence mechanism and the reasons behind the red-shift and broadening of the spectra were studied in detail. By analyzing the crystallinity and particle size distributions of the samples, as well as the Cr³⁺ ion energy level shift, it was determined that cation inversion is an effective method that can be used to tune the luminescence performance. Meanwhile, a super broad near infrared light emitting diode (LED) with a FWHM of 260 nm was obtained by combining a GaN chip with MYG:0.40Cr³⁺.

A series of broad emission band near infrared materials Mg₃Y₂Ge₃O₁₂:Cr³⁺ (650–1200 nm) was prepared based on cation inversion.

Applications of Photonics in Agriculture Sector: A Review

The agricultural industry has made a tremendous contribution to the foundations of civilization. Basic essentials such as food, beverages, clothes and domestic materials are enriched by the agricultural industry. However, the traditional method in agriculture cultivation is labor-intensive and inadequate to meet the accelerating nature of human demands. This scenario raises the need to explore state-of-the-art crop cultivation and harvesting technologies. In this regard, optics and photonics technologies have proven to be effective solutions. This paper aims to present a comprehensive review of three photonic techniques, namely imaging, spectroscopy and spectral imaging, in a comparative manner for agriculture applications. Essentially, the spectral imaging technique is a robust solution which combines the benefits of both imaging and spectroscopy but faces the risk of underutilization. This review also comprehends the practicality of all three techniques by presenting existing examples in agricultural applications. Furthermore, the potential of these techniques is reviewed and critiqued by looking into agricultural activities involving palm oil, rubber, and agro-food crops. All the possible issues and challenges in implementing the photonic techniques in agriculture are given prominence with a few selective recommendations. The highlighted insights in this review will hopefully lead to an increased effort in the development of photonics applications for the future agricultural industry.

Impact of polyphenols on the headspace concentration of aroma compounds in apple cider

BACKGROUND

To study the effect of polyphenols on the release of aroma compounds in apple cider, the impact of (-)-epicatechin, hydrocaffeic acid and phloridzin on volatility of 12 typical aroma compounds was investigated by headspace solid phase microextraction and gas chromatography-mass spectrometry.

RESULTS

Analysis of variance results showed that increased concentrations of the phenolic compounds significantly affected the headspace concentration of most aroma compounds. The three polyphenols induced a volatility decrease for the majority of hydrophobic aroma compounds, while they exhibited an opposite behavior by salting out some hydrophilic alcohols. (-)-Epicatechin and hydrocaffeic acid showed a higher retention effect on most hydrophobic aroma compounds than phloridzin did.

CONCLUSION

This study showed that the polyphenols had varying effects on aroma compound volatility in apple cider. The physicochemical characteristics and spatial conformation of polyphenols and aroma compounds influenced the magnitude of aroma-polyphenol interaction in apple cider. Understanding the effects of polyphenols on aroma release can improve the prediction of aroma profiles through chemical analysis, which assists cidermakers in improving the aroma quality of apple cider. © 2018 Society of Chemical Industry.

Double perovskite Cs2AgInCl6:Cr3+: broadband and near-infrared luminescent materials

A Cr³⁺-doped halide double perovskite Cs₂AgInCl₆:Cr³⁺ is first reported which exhibits a broad near-infrared emission ranging from 850 to 1350 nm centered at 1010 nm with a FWHM of 180 nm.

FT-IR, Vis spectroscopy, color and multivariate analysis for the control of ageing processes in distinctive Spanish wines

During the ageing period, diverse physicochemical changes occur affecting the quality of the final product. For this reason, it is important to study and optimize this step. Fourier transform infrared (FT-IR) and UV-visible (UV-Vis) spectroscopic techniques combined with multivariate analysis were used to obtain regression models to correlate both spectroscopic data and chromatic parameters with the ageing level of high quality Sherry wines. Three spectral ranges were obtained that contain the highest variance: two different fingerprint ranges in FT-IR (1100-2000 cm^-1 and 2300-2999 cm^-1) and one range in the visible region (380-450 nm). The regression model has enabled full differentiation between the seven levels of ageing in the wine explored. A good linear regression fit (R² above 0.95) was obtained regardless of the ranges used. The results demonstrate that both spectroscopic techniques can be used to optimize the ageing process in a simple and fast way.

Analysis of Water, Ethanol, and Fructose Mixtures Using Nondestructive Resonant Spectroscopy of Mechanical Vibrations and a Grouping Genetic Algorithm

A new haptic sensor that is based on vibration produced by mechanical excitation from a clock coupled to a resonant cavity is presented. This sensor is intended to determine the chemical composition of liquid mixtures in a completely non-destructive method. In this case, a set of 23 samples of water, ethanol, and fructose mixtures has been used to simulate different kinds of alcoholic beverage. The spectral information from the vibrational absorption bands of liquid samples is analyzed by a Grouping Genetic Algorithm. An Extreme Learning Machine implements the fitness function that is able to classify the mixtures according to the concentration of ethanol and fructose. The 23 samples range from 0%⁻13% by volume of ethanol and from 0⁻3 g/L of fructose, all of them with different concentration. The new technique achieves an average classification accuracy of 96%.

Rapid Determination of Active Compounds and Antioxidant Activity of Okra Seeds Using Fourier Transform Near Infrared (FT-NIR) Spectroscopy

Okra seeds (OSD) have been proved to possess significantly anti-fatigue activity and due to their high contents of flavonoids and polyphenols. While, the quality of OSD is easily affected by harvest time, region and other factors. In this research, the rapid method based on Fourier transform near infrared (FT-NIR) spectroscopy was developed for quality assessment of okra seeds. Firstly, 120 samples' spectra were acquired, and quantification of isoquercitrin, quercetin-3-O-gentiobioside, total phenols (TP) and antioxidant assays including 1-diphenyl-2-picrylhydrazyl (DPPH) scavenging, ferric reducing antioxidant power (FRAP) were conducted. Next, partial least squares (PLS) regression and full cross-validation were applied to develop calibration models for these data, and external validation was used to determine models' quality. The coefficient of determination for calibration ( R c 2 ), the root mean square error of cross validation (RMSECV) and the corresponding determination coefficients for cross-validation ( R cv 2 ) proved all these models have excellent precision. Besides, the residual predictive deviation (RPD) of models (4.07 for isoquercitrin, 4.04 for quercetin-3-O-gentiobioside, 9.79 for TP, 4.58 for DPPH and 4.12 for FRAP) also demonstrated that these models possessed good predicative ability. All these results showed that FT-NIR spectroscopy could be used to rapidly determine active compounds and antioxidant activity of okra seeds.

Molecular and Functional Characterization of Three Odorant-Binding Protein from Periplaneta americana

The American cockroach, Periplaneta americana, is a vector of many pathogenic organisms associated with human diseases. Olfaction plays a crucial role in guiding cockroach behaviors and contributes to their ability to transmit pathogens. Odorant binding proteins (OBPs), abundant in the insect olfactory sensilla, are important for insect olfaction. In this study, three OBP genes, PameOBP1, 2 and 3, were cloned from P. americana. Sequence alignment and phylogenetic analysis revealed that PameOBP1, 2 and 3 belong to the Minus-C OBP, Classic OBP, and Plus-C OBP subfamilies, respectively. Expression pattern and ligand-binding analysis showed that PameOBP1 and 2 were specifically expressed in antennae, and exhibited high binding affinities (Ki < 2 μM) to farnesene, farnesol, 2-tridecanone, and tetradecane, suggesting roles in volatile perception. Conversely, PameOBP3 was ubiquitously expressed in most of the tissues examined at high levels and displayed very weak binding affinities (Ki > 40 μM) for all 87 ligands tested. Our study provides insights into the functional diversity of PameOBP genes and provides some volatiles that can potentially be used in behavioral interference of P. americana.