FT-NIR Spectroscopy Research Articles

Rapid quantification of oil content in castor seeds using FT-NIR spectroscopy

Rapid quantification of oil content in castor seeds using FT-NIR spectroscopy

Comparison of Different Spectral Ranges to Monitor Alcoholic and Acetic Fermentation of Red Grape Must Using FT-NIR Spectroscopy and PLS Regression

AbstractWine vinegar is produced through a two-phase fermentation of grape must: initially, yeast converts grape sugars into ethanol, and subsequently, acetobacteria oxidize ethanol into acetic acid. This process, spanning weeks when conducted by surface fermentation, requires constant monitoring of ethanol and total acidity levels. To enhance the quality and efficiency of process monitoring, vinegar production is shifting to faster, environmentally sustainable methods. Near-infrared (NIR) spectroscopy, recognized for its non-invasiveness and speed, is ideal for online implementation in process control. This study tracked dual fermentation in red grape must over an extended period, monitoring two different batches simultaneously to assess fermentation kinetics and reproducibility. Ethanol content and total acidity were analyzed in fermenting musts throughout the whole fermentation process using both classical laboratory analyses and FT-NIR spectroscopy. Principal Component Analysis (PCA) was used to explore the spectral dataset, then Partial Least Squares (PLS) was used to develop calibration models for predicting ethanol and acidity. The models calculated considering the entire spectral range were compared with those obtained for two narrower zones, where more cost-effective and easily miniaturizable sensors are available on the market. FT-NIR allowed to effectively determine ethanol content and acidity (R2Pred > 0.98), both over the entire range (12,500–4000 cm−1, corresponding to 800–2500 nm) and in the 10,526–6060 cm−1 (950–1650 nm) region. Although less satisfactory, still acceptable results were obtained in the 12,500–9346 cm−1 (800–1070 nm) region (R2Pred > 0.81), confirming the potential for cost-effective devices in real-time fermentation monitoring.

Geographical origin identification of Khao Dawk Mali 105 rice using combination of FT-NIR spectroscopy and machine learning algorithms

The mislabelled Khao Dawk Mali 105 rice coming from other geographical region outside the Thung Kula Rong Hai region is extremely profitable and difficult to detect; to prevent retail fraud (that adversely affects both the food industry and consumers), it is vital to identify geographical origin. Near infrared spectroscopy can be used to detect the specific content of organic moieties in agricultural and food products. The present study implemented the combinatorial method of FT-NIR spectroscopy with chemometrics to identify geographical origin of Khao Dawk Mali 105 rice. Rice samples were collected from 2 different region including the north and northeast of Thailand. NIR spectra data were collected in range of 12,500 – 4,000 cm−1 (800–2,500 nm). Five machine learning algorithms including linear discriminant analysis (LDA), partial least squares discriminant analysis (PLS-DA), C-support vector classification (C-SVC), backpropagation neural networks (BPNN), hybrid principal component analysis-neural network (PC-NN) and K-nearest neighbors (KNN) were employed to classify NIR data of rice samples with full wavelength and selected wavelength by Extremely Randomized Trees (Extra trees) algorithm. Based on the findings, geographical origin of rice could be specified quickly, cheaply, and reliably using combination of NIRS and machine learning. All models creating by full wavelength and selected wavelength exhibited accuracy between 65 and 100 % for identifying geographical region of rice. It was proven that NIR spectroscopy may be used for the quick and non-destructive identification of geographical origin of Khao Dawk Mali 105 rice.

Exploring Deep Learning to Predict Coconut Milk Adulteration Using FT-NIR and Micro-NIR Spectroscopy.

Accurately identifying adulterants in agriculture and food products is associated with preventing food safety and commercial fraud activities. However, a rapid, accurate, and robust prediction model for adulteration detection is hard to achieve in practice. Therefore, this study aimed to explore deep-learning algorithms as an approach to accurately identify the level of adulterated coconut milk using two types of NIR spectrophotometer, including benchtop FT-NIR and portable Micro-NIR. Coconut milk adulteration samples came from deliberate adulteration with corn flour and tapioca starch in the 1 to 50% range. A total of four types of deep-learning algorithm architecture that were self-modified to a one-dimensional framework were developed and tested to the NIR dataset, including simple CNN, S-AlexNET, ResNET, and GoogleNET. The results confirmed the feasibility of deep-learning algorithms for predicting the degree of coconut milk adulteration by corn flour and tapioca starch using NIR spectra with reliable performance (R2 of 0.886-0.999, RMSE of 0.370-6.108%, and Bias of -0.176-1.481). Furthermore, the ratio of percent deviation (RPD) of all algorithms with all types of NIR spectrophotometers indicates an excellent capability for quantitative predictions for any application (RPD > 8.1) except for case predicting tapioca starch, using FT-NIR by ResNET (RPD < 3.0). This study demonstrated the feasibility of using deep-learning algorithms and NIR spectral data as a rapid, accurate, robust, and non-destructive way to evaluate coconut milk adulterants. Last but not least, Micro-NIR is more promising than FT-NIR in predicting coconut milk adulteration from solid adulterants, and it is portable for in situ measurements in the future.

Classification and quantification of adulterants in Alaska pollock based on fourier transform near-infrared spectroscopy with chemometrics

Alaska pollock is one of the most economically valuable and consumed cod species, but the adulterants in the market affect its quality and value. This study used a combination of FT-NIR spectroscopy and chemometrics to construct a qualitative and quantitative analysis model. Before the establishment of detection models, different pre-processing techniques were used to eliminate background interference. Principal component analysis (PCA) and analysis of variance (ANOVA) showed the feasibility of spectral methods for the detection of Alaska pollock adulteration. The modeling results show that the RF classification model had the most suitable prediction results (accuracy: 92.18%, precision: 98.04%, recall: 92.59%, and F1-score: 95.29%), and SVR was the optimal prediction model for quantitative analysis, with an RC2 of 0.941, an RMSEC of 0.060, an RP2 of 0.946, and an RMSEP of 0.062. These results indicate that, based on the developed detection model, adulterants in Alaska pollock could be rapidly detected and accurately quantified. In addition, it provides a reliable basis for rapid and non-destructive detection of and has great potential for determining other seafood species.

Geographic origin classification and simultaneous determination of methylxanthines in vietnamese tea using chemometrics based on the near infrared reflectance spectroscopy

This paper reported the results of classification of geographic origin and simultaneous analysis of three methylxanthines (caffeine, theobromine, theophylline) in Vietnamese tea samples by the infrared reflectance spectrophotometry coupled with chemometrics. The spectral range was 10,000-4,000cm-1 and each spectrum was measured at 2 cm-1 intervals. For the purpose of geographic origin classification, this study used FT-NIR spectroscopy combined with Partial Least Squares Discriminant Analysis (PLS-DA), and Principal Component Analysis-Discriminant Analysis (PCA-DA). The ability to determine the origin of tea samples in the prediction set of PLS-DA model is 100%. Using the same IR spectral database combined with the partial least squares (PLS), three methylxanthines in tea samples are also quickly quantified. The PLS model based on the spectra of 24 tea samples in which the contents of 3 analytes were determined by high performance liquid chromatography- HPLC) were applied for simultaneous determination of caffeine, theobromine and theophylline in samples. The determination of methylxanthines in 7 tea samples in test set gave the good accuracy of the PLS model. The correlation coefficients (R2) in the prediction set were of 0.9582, 0.8894 and 0.9303 for theobromine, theophylline, and caffeine, respectively. This work demonstrated that infrared reflectance spectrophotometry combined with chemometrics could be applied to rapidly classify the geographic origin and simultaneous determination of main contents in green tea.

Alternative identification of wood from natural fallen trees of the Lecythidaceae family in the Central Amazonian using FT-NIR spectroscopy

The scientific identification of natural fallen trees in tropical forests is complex due to the lack of fertile material in field collection. The study evaluated the use of near-infrared spectroscopy with Fourier-transform (FT-NIR) in the discrimination of wood from fallen trees of the Lecythidaceae family. Seven trees were collected in the Central Amazonian region (Brazil), from which 63 specimens were prepared from the wood, and NIR spectra were obtained on different wood surfaces (total 756 spectra). Chemometric models were developed with a spectral data set, and the Mahalanobis algorithm was applied. The discriminant model with 2nd derivative spectra improved the identification capacity, resulting in errors &lt; 5% in the identification of genus Couratari (3 ssp.), Eschweilera (2 ssp.), Holopyxidium (1 sp.) and Lecythis (1 sp.). The comparison of the spectral signatures of samples of fallen trees and wood library revealed that even when wood was exposed to environmental weathering, around 50% of the original bands were preserved, favouring discrimination at the genus level. The accuracy of the chemometric models developed indicates the applicability of FT-NIR spectroscopy integrative in identifying fallen trees from the Lecythidaceae family in the tropical forests.

A rapid and nondestructive method for the prediction of lignin content in tropical Amazon woods using FT-NIR spectroscopy

A rapid and nondestructive method for the prediction of lignin content in tropical Amazon woods using FT-NIR spectroscopy

Strategies for the quality control of Chrysanthemi Flos: Rapid quantification and end-to-end fingerprint conversion based on FT-NIR spectroscopy.

Chrysanthemi Flos (CF) is widely used as a natural medicine or tea. Due to its diverse cultivation regions, CF exhibits varying quality. Therefore, the quality and swiftness in evaluation holds paramount significance for CF. The aim of the study was to construct a comprehensive evaluation strategy for assessing CF quality using HPLC, near-infrared (NIR) spectroscopy, and chemometrics, which included the rapid quantification analyses of chemical components and the Fourier transform (FT)-NIR to HPLC conversion of fingerprints. A total of 145 CF samples were utilised for data collection via NIR spectroscopy and HPLC. The partial least squares regression (PLSR) models were optimised using various spectral preprocessing and variable selection methods to predict the chemical composition content in CF. Both direct standardisation (DS) and PLSR algorithms were employed to establish the fingerprint conversion model from the FT-NIR spectrum to HPLC, and the model's performance was assessed through similarity and cluster analysis. The optimised PLSR quantitative models can effectively predict the content of eight chemical components in CF. Both DS and PLSR algorithms achieve the calibration conversion of CF fingerprints from FT-NIR to HPLC, and the predicted and measured HPLC fingerprints are highly similar. Notably, the best model relies on CF powder FT-NIR spectra and DS algorithm [root mean square error of prediction (RMSEP) = 2.7590, R2 = 0.8558]. A high average similarity (0.9184) prevails between predicted and measured fingerprints of test set samples, and the results of the clustering analysis exhibit a high level of consistency. This comprehensive strategy provides a novel and dependable approach for the rapid quality evaluation of CF.

Non-Saccharomyces yeast derivatives: Characterization of novel potential bio-adjuvants for the winemaking process

Winemakers have access to a diverse range of commercially available Inactivated Dry Yeast Based products (IDYB) from various companies and brand names. Among these, thermally inactivated dried yeasts (TIYs) are utilized as yeast nutrients during alcoholic fermentation, aiding in the rehydration of active dry yeasts and reducing ochratoxin A levels during wine maturation and clarification. While IDYB products are generally derived from Saccharomyces spp., this study investigates into the biodiversity of those deriving from non-Saccharomyces for potential applications in winemaking. For that S. cerevisiae and non-Saccharomyces TIYs were produced, characterized for nitrogen and lipid content using FT-NIR spectroscopy, and applied in a wine-like solution (WLS) for analyzing and quantifying released soluble compounds. The impact of TIYs on oxygen consumption was also assessed. Non-Saccharomyces TIYs exhibited significant diversity in terms of cell lipid composition, and amount, composition, and molecular weight of polysaccharides. Compared to that of S. cerevisiae, non-Saccharomyces TIYs released notably higher protein amounts and nHPLC-MS/MS-based shotgun proteomics highlighted the release of cytosolic proteins, as expected due to cell disruption during inactivation, along with the presence of high molecular weight cell wall mannoproteins. Evaluation of antioxidant activity and oxygen consumption demonstrated significant differences among TIYs, as well as variations in GSH and thiol contents. The Principal Component Analysis (PCA) results suggest that oxygen consumption is more closely linked to the lipid fraction rather than the glutathione (GSH) content in the TIYs. Overall, these findings imply that the observed biodiversity of TIYs could have a significant impact on achieving specific oenological objectives.

Development of Rapid Grading Method For Quality Analysis of Ghee Using Fourier Transform Near Infra Red (FT-NIR) Spectroscopy

Ghee is one of the important fat sources in our diet. The ghee loses its quality due to increased rancidity. Hence, a rapid grading method is required in the industry since the quality of the ghee varies according to its storage time in the market. The present study aimed to develop the rapid grading method using FT-NIR spectroscopy. The quality of the ghee was graded with two parameters mainly moisture and FFA. The FFA and moisture content of the ghee was analyzed with the chemical method, and it was used to develop the spectra for grading of ghee. The spectra of the samples were obtained and the model was calibrated and validated. The performance of the model was analyzed by R2 and RMSEcv.The coefficient of determination (R2) was &gt; 95 % for moisture and FFA showed higher accuracy. The Residual Predictive Deviation(RPD) of the model was 5.71 and 6.36 for moisture and FFA, respectively which is greater than 3 showed the excellence of prediction. The time taken for the FT-NIR to analyze the moisture and FFA of the unknown sample was 25 s. The FT-NIR was useful for rapid quality grading of ghee.

Data fusion of FT-NIR spectroscopy and Vis/NIR hyperspectral imaging to predict quality parameters of yellow flesh “Jintao” kiwifruit

The internal quality of kiwifruit, in terms of soluble solid content (SSC), flesh firmness (FF), and dry matter (DM), is widely recognised as a key feature for fruit sorting and pre-harvest assessment. Furthermore, flesh hue (FH) is another important parameter to consider for yellow flesh kiwifruits. NIR and VIS/NIR spectroscopic techniques are valuable alternatives for rapid and non-destructively prediction of all these quality parameters in fruit. Accordingly, the aim of this work was to build a partial least square (PLS) regression models to estimate SSC, FF, FH, and DM of yellow fleshed Actinida chinensis (Jintao) starting from Vis/NIR hyperspectral imaging (400–1000 nm) and FT-NIR (800–2500 nm) spectroscopy data. To take advantage of the complementary information of the two different spectral ranges, data fusion strategies were investigated to concatenate the data before PLS models. In particular, two different sequential fusion methods were used: low-level data fusion based on the concatenation of the pretreated spectra, and mid-level feature fusion characterised by the concatenation of features (scores) obtained by principal component analysis (PCA) or PLS models developed considering individually each data set. For all quality parameters, the best results were achieved by adopting the second approach of mid-level data fusion (PLS scores), reporting RP2 (test set validation) of 0.914 (RMSEP=0.97°Brix), 0.843 (RMEP=1.82°H), 0.866 (RMSEP=9.41N), and 0.854 (RMSEP = 0.64%) for SSC, FH, FF, and DM, respectively. Furthermore, with respect to the PLS models from the individual data sets, the results reported a mean RMSEP reduction of 16.0 ± 4.8%, confirming the potential of the data fusion in improving the PLS prediction power for the quality parameter of kiwifruit.

Detection and quantification of corn starch and wheat flour as adulterants in milk powder by near- and mid-infrared spectroscopy coupled with chemometric routines

Adulterating foods, such as milk powder (MP), is a common practice in countries with no rigid policy on food quality control. This study employs Fourier transform near-infrared and mid-infrared (FT-NIR, FT-MIR) spectroscopy and chemometric analysis to detect milk powder adulteration with corn starch (CS) and wheat flour (WF) from 0.00 to 30.00% w/w concentrations. Partial least square regression (PLSR) models were developed, optimized, and compared to quantify corn starch and wheat flour adulterations. According to the results, the root mean square error prediction (RMSEP) for FT-NIR and FT-MIR in corn starch was 0.74 and 1.69% w/w and 0.82 and 2.63% w/w for wheat flour, respectively. FT-NIR spectroscopy, rather than FT-MIR coupled with the appropriate chemometrics models represents a more valuable tool for simple, rapid, and nondestructive detection of adulterants in milk powder. The recent availability of portable instruments, combined with suitable chemometric tools, makes it possible to discriminate adulterated food samples in situ.

Application of near infrared spectroscopy for identification and quantitative determination of amino acids in their crystalline and salt forms in the preparation of animal feed

Methods. For the first time, a comprehensive calibration model has been developed and presented for the rapid determination of basic near-infrared spectroscopy (IR) methods in feed amino acids, with application in the production of animal feed. The research principle is based on the Fourier equation for spectroscopy. In this work, Fourier methods in the Italian region (FTIR, FT-NIRS) were applied. The data obtained from the calibration models were confirmed using high-performance liquid chromatography. FT-NIR predictions agreed well with the chromatography data and had predictive deviation (RPD) values &gt;1.3 in all cases.Results. The results indicate that FT-NIR spectroscopy can be used as a simple and rapid tool for monitoring amino acids. In the course of the work, experimental confirmation of previously known facts was obtained — the possibility of visual separation of the spectra of not only counterfeit amino acids, but also the possibility of separation by L- and DL-optical isomers. The work shows that the discrepancies between the values obtained by the classical “wet chemistry” method and the values obtained from the constructed calibration models do not exceed the reproducibility limits of arbitration methods. A predictive model was used based on information flow elements using the OPUS/QUANT2 software package for multivariate calibration and construction of calibration models for amino acids. This chemometric analysis proved the fundamental possibility of determining amino acids in the product. It has been shown that the use of information channels opens up opportunities for the use of many chemometrics algorithms, including data preprocessing and the construction of predictive models.

Prediction of glucose and sucrose values of black tea samples using NIR spectroscopy and chemometrics

FT-NIR and chemometrics are considered one of the most important analytical tools for the quality control of tea. The present study focused on developing and validating a rapid quantification method for the quantification of sugar, specifically sucrose and glucose in black tea using FT-NIR spectroscopy. Calibration models were established by partial least squares (PLS) regression analysis. Fructose, glucose, and sucrose concentrations in black tea were analyzed using high-performance liquid chromatography. The spectral range and the number of PLS factors were optimized for the Residual Predictive Deviation (RPD) and correlation coefficient (r2) of determination. The NIR spectral region between 5878 and 3996 cm−1 (1701–2500 nm) was selected as the best range for predicting glucose, sucrose, and fructose concentrations in black tea. The calibration model developed for sucrose has high RPD (4.29) and r2 (0.95) indicating the potential for predicting the inherent sucrose content in black tea with high accuracy. The calibration model developed for glucose has RPD of 2.73 and r2 (0.87) showing the potential for predicting glucose content in black tea with reasonable accuracy. Both calibration models could be useful for quality assurance purposes in black tea, and easier and faster than the wet chemical HPLC method. FT-NIR spectroscopy coupled with chemometrics has been demonstrated for the first time to quantify the inherent glucose and sucrose concentrations in black tea. Calibration models could be used to determine adulteration of black tea with sucrose and glucose.

An integrated chemical characterization based on FT-NIR, and GC–MS for the comparative metabolite profiling of 3 species of the genus Amomum

BackgroundThe fruits and seeds of genus Amomum are well-known as medicinal plants and edible spices, and are used in countries such as China, India and Vietnam to treat malaria, gastrointestinal disorders and indigestion. The morphological differences between different species are relatively small, and technical characterization and identification techniques are needed. ResultsFourier transform near infrared spectroscopy (FT-NIR) and gas chromatography-mass spectrometry (GC-MS), combined with principal component analysis and two-dimensional correlation analysis were used to characterize the chemical differences of Amomum tsao-ko, Amomum koenigii, and Amomum paratsaoko. The targets and pathways for the treatment of diabetes mellitus in three species were predicted using network pharmacology and screened for the corresponding pharmacodynamic components as potential quality markers. The results of “component-target-pathway” network showed that (+)-Nerolidol, 2-Nonanol, α-Terpineol, α-Pinene, 2-Nonanone had high degree values and may be the main active components. Partial least squares-discriminant analysis (PLS-DA) was further used to select for differential metabolites and was identified as a potential quality marker, 11 in total. PLS-DA and residual network (ResNet) classification models were developed for the identification of 3 species of the genus Amomum, ResNet model is more suitable for the identification study of large volume samples. SignificanceThis study characterizes the differences between the three species in a visual way and also provides a reliable technique for their identification, while demonstrating the ability of FT-NIR spectroscopy for fast, easy and accurate species identification. The results of this study lay the foundation for quality evaluation studies of genus Amomum and provide new ideas for the development of new drugs for the treatment of diabetes mellitus.

Comparative Analysis of LC-ESI-IM-qToF-MS and FT-NIR Spectroscopy Approaches for the Authentication of Organic and Conventional Eggs.

The importance of animal welfare and the organic production of chicken eggs has increased in the European Union in recent years. Legal regulation for organic husbandry makes the production of organic chicken eggs more expensive compared to conventional husbandry and thus increases the risk of food fraud. Therefore, the aim of this study was to develop a non-targeted lipidomic LC-ESI-IM-qToF-MS method based on 270 egg samples, which achieved a classification accuracy of 96.3%. Subsequently, surrogate minimal depth (SMD) was applied to select important variables identified as carotenoids and lipids based on their MS/MS spectra. The LC-MS results were compared with FT-NIR spectroscopy analysis as a low-resolution screening method and achieved 80.0% accuracy. Here, SMD selected parts of the spectrum which are associated with lipids and proteins. Furthermore, we used SMD for low-level data fusion to analyze relations between the variables of the LC-MS and the FT-NIR spectroscopy datasets. Thereby, lipid-associated bands of the FT-NIR spectrum were related to the identified lipids from the LC-MS analysis, demonstrating that FT-NIR spectroscopy partially provides similar information about the lipidome. In future applications, eggs can therefore be analyzed with FT-NIR spectroscopy to identify conspicuous samples that can subsequently be counter-tested by mass spectrometry.

Complex Evaluation of Storage Impact on Maize (Zea mays L.) Quality Using Chromatographic Methods

Maize (Zea mays L.), a food crop cultivated worldwide, is renowned for its nutritional and economic value. However, its quality can be significantly affected by various storage conditions, leading to changes in its nutritional composition and potential contamination with harmful substances, such as aflatoxins. This article presents a comprehensive evaluation of the impact of storage on maize quality, focusing on several key parameters analyzed using liquid chromatographic methods and FT-NIR spectrometry. The research reveals a decline in the concentrations of key nutrients over the storage period. Specifically, there was a 19.7% loss in vitamin B1, a 12.43% loss in vitamin B3, a 16.96% loss in α-tocopherol, a 13.61% loss in total tocopherols, and an 8.02% loss in β-carotene. Aflatoxins were mostly undetectable, with one exception in January that remained below the maximum permitted level of 0.3 µg/kg. The parameters include the concentration of vitamins B1, B3, α-tocopherol, total tocopherols, β-carotene, aflatoxin B1, total aflatoxins, carbohydrate content, and protein content. Aflatoxins were mostly undetectable, with one exception in January—0.3 µg/kg—that remained below the maximum permitted level. The study underscores the importance of cooling maize immediately after drying to minimize nutrient loss, providing valuable insights for optimizing storage processes to maintain the nutritional quality of maize.

Classification of waste wood categories according to the best reuse using FT-NIR spectroscopy and chemometrics

In Europe, the volume of waste wood is increasing. Waste wood can be reused, promoting circular economy and avoiding landfills. It can be used as a bioenergy feedstock reducing the use of fossil fuels, or be reused for producing new composite wood material. Only wood with hazardous substances needs to be disposed. To this aim waste wood samples were collected from a panel board company and several recycling centres in Italy and Denmark. The samples were assigned to waste wood categories and analysed by Near Infrared Spectroscopy. Principal Component Analysis was used to investigate sample variability and Soft Independent Modelling of Class Analogies (SIMCA) for classifying the samples according to the appropriate reuse: energy production, panel board production or landfill. The results are good, with a classification rate of 90% for virgin wood material and 86.7% for treated wood material. The classification of waste wood is key for turning it into a secondary resource.

Spectroscopic Non-targeted Techniques in Combination with Linear Discriminant Analysis for Wine Vinegar Authentication

This work aims at comparing different non-targeted spectroscopic techniques (i.e., UV–Vis, FT-IR, FT-NIR, and NIR spectroscopy) for the authentication of white wine vinegar. Five white wine vinegars were adulterated with two different spirit vinegars. Further twenty-five wine vinegars were analyzed to enlarge the authentic product dataset. All samples (i.e., 160) were analyzed in duplicate by UV–Vis, FT-NIR, and FT-IR spectroscopy; moreover, a handheld NIR device was tested on a subset of samples (i.e., 89). Principal component analysis revealed sample patterns related to vinegar acidity (6 or 7.1%) rather than adulteration levels. After variable selection (SELECT algorithm), linear discriminant analysis (LDA) models were developed and tested by independent external sets. The LDA models gave very high weighted correct classification rates in calibration (95.5–100.0%), cross-validation (92.4–100.0%), and prediction (90.0–100.0%) for all the spectroscopic techniques. With the portable NIR instrument, 100% correct classifications in prediction were obtained, demonstrating its suitability in vinegar authentication.