Near-infrared Technique Research Articles

Monitoring the Shelf Life of Hemp Seed Oil Stored at Two Temperatures in Different Materials via Near-Infrared (NIR) Spectroscopy

Hempseed oil (HSO) is extremely rich in unsaturated fatty acids, especially linoleic (18:2 n-6) and α-linolenic (18:3 n-3) acids, which determine its high sensitivity to oxidative and photo-oxidative degradations that can lead to rancidity despite the presence of antioxidant compounds. The aim of this work was to evaluate which material/temperature/light solutions better preserve HSO quality during its shelf life and to test NIR as a rapid, non-destructive technique for monitoring oxidation phenomena. Futura 75 hemp seeds were cold-pressed; the oil was packed into 20 mL vials of four different materials (polypropylene, clear glass, amber glass, and amber glass coated with aluminum foil) and stored for 270 days at 25 °C under diffused light and at 10 °C in dark conditions., Peroxides and conjugated dienes and trienes were evaluated at intervals to monitor oil stability. Moreover, NIR spectra were measured in transmission, and the sample dataset was analyzed using ASCA to test the significance of the experimental factors: the model showed the significance of all factors and of all the simple interactions. Our results demonstrate that oil stored in amber glass vials with aluminum foils at refrigerated temperatures receive the highest protection from environmental conditions, mitigating oxidative changes, and that the NIR technique could be used to rapidly monitor HSO oxidation parameters.

Advances in Spectroscopic Methods for Predicting Cheddar Cheese Maturity: A Review of FT-IR, NIR, and NMR Techniques

The quest for reliable techniques to predict Cheddar cheese maturity has gained momentum to ensure quality and consistency in large-scale production. Given the complexity of cheese ripening and the industry’s need for fast and reliable evaluation methods, this review addresses the challenge by scrutinising the application of spectroscopic techniques such as Fourier transform infrared (FT-IR), near-infrared (NIR), and nuclear magnetic resonance (NMR). These methods are evaluated for their noninvasive and rapid on-site analysis capabilities, which are essential for ensuring quality in cheese production. This review synthesises current research findings, discusses the potential and limitations of each technique, and highlights future research directions. Overall, NIR spectroscopy emerges as the most promising, offering quick, nondestructive assessments and reasonably accurate compositional predictions, crucial for real-time maturation monitoring. It provides rapid results within minutes, making it significantly faster than FT-IR and NMR. While FT-IR also offers high accuracy, it typically requires longer analysis times due to extensive calibration and can be sensitive to sample conditions, while NMR, although highly accurate, involves complex and time-consuming procedures. Nonetheless, further studies are necessary to refine these spectroscopic techniques, enhance their predictive accuracy, and deepen the understanding of the correlations between chemical attributes and sensory qualities in Cheddar cheese.

Comparison of qualitative and quantitative performance of two portable near-infrared spectrometers for intact Rehmanniae Radix and calibration transfer

As a Chinese herbal medicine with high medical value, Rehmanniae Radix (RR) has a wide variety of geographical origins leading to distinctly diverse quality, and moisture content during storage affects the critical active ingredient content. In this work, two portable near-infrared (NIR) spectrometers, DLP NIRscan Nano (DLP) and VIAVI MicroNIR 1700 (M1700) were used for in-situ spectral acquisition from intact RR. The partial least squares-discriminant analysis (PLS-DA) was employed for three geographical sources (Shandong, Shanxi, Henan). After multiple pre-processing and wavelength selection, the M1700-based PLS-DA model, accuracy (Acc), sensitivity (Sen), and specificity (Spe), achieved 100 % accuracy. In contrast, model predictions using the low-cost instrument (DLP) was not satisfactory. The partial least squares regression (PLSR) was applied to predict the moisture content of RR. The best correlation coefficients in the prediction set (R2p), Root Mean Square Error of Prediction (RMSEP), and residual prediction deviation (RPD) values (0.98, 0.98, 4.98) were obtained by DLP and were also employed for comparison with the M1700 model (0.99, 0.70, 7.00). Therefore, to further improve the model prediction effect of the low-cost DLP, we employed the improved principal component analysis (IPCA), direct standardization (DS), and piecewise direct standardization (PDS) calibration transfer techniques. Unequivocally, IPCA optimized the origin identification model (Acc, Sen, and Spe were all 1.00), the moisture content prediction model RPD was increased by 23 %, and its RMSEP was reduced by 18 %, leading to the improvement in prediction accuracy of the DLP model. This study provides a portable and low-cost detection method for the in-situ evaluation of the quality of RR quality and a feasible solution for the NIR techniques to be used in the rapid and accurate in-situ analysis of RR.

Development of near-infrared spectroscopy (NIRS) for estimating organic matter, total carbon, and total nitrogen in agricultural soil

The analysis of soil organic matter (OM), total carbon (TC), and total nitrogen (TN) using traditional methods is quite time-consuming and involves the use of hazardous chemical reagents. Absorbance spectroscopy, especially near-infrared (NIR), is becoming more popular for soil analysis. This method requires little sample preparation, no chemicals, and a single spectral analysis to evaluate soil properties. Thus, this research aimed to develop an NIR spectroscopy method for the analysis of OM, TC, and TN in agricultural soils. These findings can provide a good concept of using PLS regression with NIR techniques. The method is as follows:•Topsoil (0–20 cm) samples were collected from various agricultural fields. OM, TC, and TN were analyzed using traditional methods and NIR spectroscopy.•NIR spectra were obtained using an FT-NIR spectrometer, original spectral including with Savitzky–Golay smoothing, standard normal variate (SNV) and multiplicative scatter correction (MSC) preprocessing method were used to create a predicted model through Partial Least Squares (PLS) regression with 65 % calibration, and the rest 35 % for validation.•The results showed significant relationships between measured soil properties (SOM and TC) and NIR absorbance spectra in agricultural soil (R2 of calibration and validation higher than 0.80).

Quantitative analysis of the hexamethylenetetramine concentration in a hexamethylenetetramine–acetic acid solution using near infrared spectroscopy: A comprehensive study on preprocessing methods and variable selection techniques

Hexamethylenetetramine (HA) is widely used as a raw material in the medical, chemical, industrial, and military industries, and the fast and quantitative analysis of HA is important for manufacturing processes in these fields. Owing to its efficiency, low cost, nondestructive testing, and convenience, near infrared (NIR) spectroscopy is a powerful technique for quantitatively analyzing the HA concentration in HA–acetic acid (HAc) solutions, demonstrating application potential in the production of hexogen and octogen. A series of preprocessing algorithms and variable selection methods were studied to improve the detection accuracy of the NIR spectroscopic calibration. Forty-six different combinations of standard normal variation (SNV), multiplicative signal correction, first derivative, second derivative, and discrete wavelet transform (DWT) were screened. The effects of four variable selection methods (successive projection algorithm (SPA), uninformed variable elimination, competitive adaptive reweighted sampling, and multiverse optimization (MVO)) were compared. Finally, a model (SPXY-SNV-1stDer-DWT-MVO-RF) was developed by combining sample set portioning based on the joint x–y distance (SPXY) algorithm with the random forest (RF) calibration model, and MVO was combined with the NIR technique for the first time. The model achieved a coefficient of determination for the calibration set (R2), root mean square error of the calibration set (RMSEC), coefficient of determination for the prediction set (r2), and root mean square error of the prediction set (RMSEP) of 1.000, 0.04%, 0.999, and 0.05%, respectively. This study demonstrated the novelty and feasibility of HA quantitative detection by NIR spectroscopy and provided valuable insights for optimizing quantitative analysis models by optimizing algorithms, indicating the great application potential of NIR spectroscopy in related fields.

Characterization of Melaleuca armillaris essential oil in Brazil

The objective of this study was to propose metrics that could be used in the quality control of Melaleuca armillarisoil, extracted from plants cultivated in Brazil, to differentiate it from Melaleuca alternifoliaoil. The oil was obtained by hydro-distillation using steam stripping. The physicochemical characteristics of the M. armillarisessential oil were evaluated, obtaining the following results: density 0.9146 g/mL, pH4.36, specific rotation 2.38, refractive index 1.45948, viscosity of 3 centipoises, 35901 particles ≥ 10 μm and 1743 particles between 10 and 25 μm/5 mL, moisture content 0.84% and decomposition temperature of 115.19 ºC. Osmolarity was not detected. The components of the M. armillarisoil were identified using the following techniques: gas chromatography (GC), high performance liquid chromatography (HPLC) with a mass detector, Raman and near infrared (NIR) spectroscopy. A total of 24 compounds were identified by GC, the main one being 1,8 cineole, and 145 substances were identified by HPLC. In the identification by Raman the transmittance of the oil was found to be 785 nm, and using the NIR technique, the greatest absorbance was at 2350 nm. In this way the M. armillarisoil was characterized by physicochemical analyses and its components identified.

Spectroscopy, a Tool for the Non-Destructive Sensory Analysis of Plant-Based Foods and Beverages: A Comprehensive Review

In recent years, there has been a significant rise in the popularity of plant-based products due to various reasons, such as ethical concerns, environmental sustainability, and health benefits. Sensory analysis is a powerful tool for evaluating the human appreciation of food and drink products. To link the sensory evaluation to the chemical and textural compositions, further quantitative analyses are required. Unfortunately, due to the destructive nature of sensory analysis techniques, quantitative evaluation can only be performed on samples that are different from those ingested. The quantitative knowledge of the analytical parameters of the exact sample ingested would be far more informative. Coupling non-destructive techniques, such as near-infrared (NIR) and hyperspectral imaging (HSI) spectroscopy, to sensory evaluation presents several advantages. The intact sample can be analyzed before ingestion, providing in a short amount of time matrices of quantitative data of several parameters at once. In this review, NIR and imaging-based techniques coupled with chemometrics based on artificial intelligence and machine learning for sensory evaluation are documented. To date, no review article covering the application of these non-destructive techniques to sensory analysis following a reproducible protocol has been published. This paper provides an objective and comprehensive overview of the current applications of spectroscopic and sensory analyses based on the state-of-the-art literature from 2000 to 2023.

Analysis of classical techniques precision on the measurement of cellulose moisture gain/loss.

The precision of the four classical techniques (Karl-Fischer titration, (thermo)gravimetric method, Fourier-transform infrared (FT-IR) and near infrared (NIR) spectroscopies) commonly used in the analysis of cellulose moisture absorption/desorption has been deeply investigated regarding the reproducibility of these processes. Based on multiple repeated experiments, cellulose water content values obtained with Karl-Fischer titration and (thermo)gravimetric method were plotted as a function of time. Then, the cautious peak-by-peak analysis of the absorbance and wavenumber shifts visible in IR spectra has been carried out. The collected data was described using boxplots that provided valuable information on the experimental points spread. It has been successfully proven that gravimetric methods allow for precise drawing of moisture absorption and desorption curves, while Karl-Fischer titration, ATR FT-IR and NIR techniques provide the possibility of the moisture absorption/desorption processes description by linear mathematical models (R2 >90%). Therefore, this study provides a systematic comparison between various analytical methods.

Assessment of Maturity of Plum Samples Using Fourier Transform Near-Infrared Technique Combined with Chemometric Methods.

The FT-NIR technique was used for rapid and non-destructive determination of plum ripeness. The dry matter (DM), titratable acidity (TA), total soluble solids (TSS) and calculated maturity index (MI: TSS/TA) were used as reference values. The PLS correlations were validated via five-fold cross-validation (RMSECV for different parameters: DM: 0.66%, w/w; TA = 0.07%, w/w; TSS = 0.72%, w/w; MI = 1.39) and test set validation (RMSEP for different parameters: DM: 0.65%, w/w TA = 0.07%, w/w; TSS = 0.61%, w/w; MI = 1.50). Different classification algorithms were performed for TA, TSS and MI. Linear, quadratic and Mahalanobis discriminant analysis (LDA, QDA, MDA) were found to be the best sample detection methods. The accuracy of the classification methods was 100% for all investigated parameters and cultivars.

Hydromechanical behavior of wood during drying studied by NIR spectroscopy and image analysis

Abstract The physical properties of wood are important parameters to qualify the material. However, as it is a heterogeneous material, moisture content and wood contractions may vary within the sample. Thus, the objective was to monitor the hydromechanical behavior of wood during drying using near infrared (NIR) spectroscopy and image analysis. Equidistant points were marked on the radial surface of a wooden board and NIR spectra were recorded at each marking during drying of the piece. After spectral acquisition in each drying step, images were obtained and the markings were referenced to monitor contractions during drying. Moisture content (MC) estimates via NIR spectra showed strong correlation with reference values (R2cv = 0.92, RMSEcv = 9.82 %). From the estimates it was possible to generate graphic images to visualize and quantify the spatial variation of MC and shrinkage during wood drying. In the initial stages of drying, the ends of the material showed high moisture in relation to the center of the sample. However, MC loss was 11 % greater at the ends of the wood board when compared to its interior while the shrinkage in external zones was 3 times greater than the internal part. The use of NIR technique associated with image analysis can be a promising tool for estimating moisture contents and contractions in wood.

Detection and characterization of adulterants in baru and soursop oils

The presence of vegetable oils in the pharmaceutical and cosmetic industries boosts their production and commercialization, maximizing the economic potential, however to increase profits, vegetable oils with high added value are the target of adulteration practices. This negatively affects the quality of the product and compromises the bioactive properties of these oils. The objective of this study was to build partial least squares regression models to identify baru oil and soursop oil samples adulterated with soybean oil. For data collection the oils samples were characterized using portable near-infrared spectrometer and Fourier-transform infrared spectrometer. These techniques were chosen because both provide information about the functional groups of chemical compounds, however portable equipment, being cheaper, presents signals with lower spectral resolution. Thus, the objective of the work is also to verify if the results using both equipment are similar. The developed regression models were effective; however, the near-infrared technique presented some limitations in the identification of soursop oil. This is because for the construction of his model it was necessary to use a smaller number of variables and levels of adulteration. Nevertheless, high R² values and relatively low errors were obtained for all models, making it possible to identify the adulteration.
 
 Pratica Application: Adulteration analysis of baru and soursop oils by infrared spectroscopy and portable equipment

Near infrared spectroscopy and multivariate statistical analysis as rapid tools for the geographical origin assessment of Italian hazelnuts

The geographical origin assessment of Italian hazelnuts is nowadays a relevant topic, aimed at the protection of provenience certificates. Near Infrared (NIR) spectroscopy could be a functional candidate for preventing and fighting illegal activities related to this matrix. The present study focuses on the exploitability of the NIR technique on the ‘hazelnut chain’ (fresh, roasted and paste), against the false origin declaration frauds, mainly concerning some of the best Italian varieties (‘Nocciola Piemonte’, ‘Tonda Gentile Romana’, ‘Mortarella’). 216 spectra were recorded, for a total of n = 144 for the training set, and n = 72 for the validation set, considering fresh (n = 57), roasted (n = 107), and paste (n = 52) hazelnuts as different matrices. The training set sample selection was made according to a Design of Experiment (DoE), that considered diverse factors, such as harvesting year, storage shelf life, and presence of peel. The validation set was composed of blended samples generated by mixing Italian and non-Italian ones, and real samples bought from local markets. Multivariate Statistical Analysis was employed for data handling and elaboration, both unsupervised and supervised models, Principal Component Analysis, and Partial Least Square-Discriminant Analysis were built to simplify, observe, and classify the samples. A variables selection was performed by filtering the most important ones considering the Variable Importance in Projection (VIP) scores. The predictive ability of the technology was evaluated by applying Classification List and Confusion Matrix approaches to a prediction set, providing a fit of the observations of this set into the selected supervised model. The outcomes highlight valuable discrimination between authentic samples (related to two different harvesting year campaigns) with classification accuracy rates between 89 % and 100 %. Promising results about the application on blended and real samples were also obtained, especially as regards fresh and roasted hazelnuts, which presented classification accuracy rates of 81 % and 91 %. Therefore, this analytical technique could play a strategic role in the geographical origin assessment considering it is a rapid, direct, non-destructive, and cost-effective approach.

Using Molecular Spectroscopic Techniques (NIR and ATR-FT/MIR) Coupling with Various Chemometrics to Test Possibility to Reveal Chemical and Molecular Response of Cool-Season Adapted Wheat Grain to Ergot Alkaloids.

The objectives of this study were to explore the possibility of using near infrared (NIR) and Fourier transform mid-infrared spectroscopy-attenuated total reflectance (ATR-FT/MIR) molecular spectroscopic techniques as non-invasive and rapid methods for the quantification of six major ergot alkaloids (EAs) in cool-season wheat. In total, 107 wheat grain samples were collected, and the concentration of six major EAs was analyzed using the liquid chromatography-tandem mass spectrometry technique. The mean content of the total EAs-ergotamine, ergosine, ergometrine, ergocryptine, ergocristine, and ergocornine-was 1099.3, 337.5, 56.9, 150.6, 142.1, 743.3, and 97.45 μg/kg, respectively. The NIR spectra were taken from 680 to 2500 nm, and the MIR spectra were recorded from 4000-700 cm-1. The spectral data were transformed by various preprocessing techniques (which included: FD: first derivative; SNV: standard normal variate; FD-SNV: first derivative + SNV; MSC: multiplicative scattering correction; SNV-Detrending: SNV + detrending; SD-SNV: second derivative + SNV; SNV-SD: SNV + first derivative); and sensitive wavelengths were selected. The partial least squares (PLS) regression models were developed for EA validation statistics. Results showed that the constructed models obtained weak calibration and cross-validation parameters, and none of the models was able to accurately predict external samples. The relatively low levels of EAs in the contaminated wheat samples might be lower than the detection limits of the NIR and ATR-FT/MIR spectroscopies. More research is needed to determine the limitations of the ATR-FT/MIR and NIR techniques for quantifying EAs in various sample matrices and to develop acceptable models.

Estimation of the Differential Pathlength Factor for Human Skin Using Monte Carlo Simulations.

Near-infrared technology is an emerging non-invasive technique utilized for various medical applications. Recently, there have been many attempts to utilize NIR technology for the continues monitoring of blood glucose levels through the skin. Different approaches and designs have been proposed for non-invasive blood glucose measurements. Light photons penetrating the skin can undergo multiple scattering events, and the actual optical pathlength becomes larger than the source-to-detector separation (optode spacing) in the reflection-mode configuration. Thus, the differential pathlength factor (DPF) must be incorporated into the modified Beer-Lambert law. The accurate estimation of the DPF values will lead to an accurate quantification of the physiological variations within the tissue. In this work, the aim was to systematically estimate the DPF for human skin for a range of source-to-detector separations and wavelengths. The Monte Carlo (MC) method was utilized to mimic the different layers of human skin with different optical properties and blood and water volume fractions. This work could help improve the accuracy of the near-infrared technique in the measurement of physiological variations within skin tissue.

Análise de trilha e espectroscopia no infravermelho próximo na cultura da canola

ABSTRACT: This study measured the effect of the association between agronomic traits related to the yield of canola grains grown at different sowing dates through path analysis. Another objective was to obtain a method to predict the oil content in the grains, fitting a multivariate model through near-infrared (NIR) spectroscopy analysis. The experiment was conducted in the field using a randomized block design in plots subdivided by time, with four plots (sowing dates), six subplots (canola hybrids), and four replicates. In each hybrid, phenological observations were performed, and the grain yield was determined. The data were subjected to analysis of variance in the R environment using the F test at 5% probability. The oil content in the grains was determined by the traditional chemical method, and based on the NIR spectral signature of the grain samples, partial least squares regression (PLS-R) was established to estimate the oil content in the canola grains. The sowing dates influenced the production components and oil content of the grains of all hybrids. The trait number of grains in five plants (0.6857) and their height (0.4943) had greater estimates of positive correlations with grain yield, as well as higher values of positive direct effects on yield (0.2494 and 0.1595, respectively). The NIR technique combined with PLS-R was able to predict the oil content in the grains, resulting in good predictive models (R2 of 0.86 and root mean square error (RMSE) of 1.56 in external validation).

Film Thickness and Glycerol Concentration Mapping of Falling Films Based on Fluorescence and Near-Infrared Technique.

Falling film evaporation processes involve high fluid velocities with continuous variations in local film thickness, fluid composition, and viscosity. This contribution presents a parallel and complementary film thickness and concentration mapping distribution in falling films using a non-invasive fluorescence and near-infrared imaging technique. The experiments were performed with a mixture of glycerol/water with a mass fraction from 0 to 0.65 gglycgtotal-1 and operating ranges similar to evaporation processes. The measurement system was designed by integrating two optical measurement methods for experimental image analysis. The film thickness was evaluated using a VIS camera and high-power LEDs at 470 nm. The local glycerol concentration gglycgtotal-1 was determined using a NIR camera and high-power LEDs at 1050, 1300, 1450 and 1550 nm. A multiwavelength analysis with all NIR wavelengths was implemented with a better correlation for falling films at low flow velocity. The results show an improvement in the analysis of falling films with high flow velocities up to almost 500 mm/s by using only the 1450 nm wavelength and the fluorescence measurement. Simultaneous imaging analysis of film thickness and concentration in falling films provides further insight into understanding mass and heat transport and thus supports the optimization of falling film evaporators.

Integrative quantitative and qualitative analysis for the quality evaluation and monitoring of Danshen medicines from different sources using HPLC-DAD and NIR combined with chemometrics.

The root and rhizome of Salvia miltiorrhiza (Danshen in short) is a well-known herbal medicine used to treat cardiovascular diseases in the world. In China, the roots and rhizomes of several other Salvia species (Non-Danshen in short) are also used as this medicine in traditional folk medicine by local herbalists. Differences have been reported in these medicines originating from different sources, and their quality variation needs to be clearly investigated for effective clinical application. This study presented a comprehensive quality evaluation and monitoring for Danshen from 27 sampling sites and Non-Danshen from other 5 Salvia species based on a high-performance liquid chromatography-diode array detector (HPLC-DAD) and near-infrared (NIR), with the combination of chemometric models. The results showed that cryptotanshinone, tanshinone IIA, tanshinone I, salvianolic acid B, salvianic acid A sodium, dihydrotanshinone I, and rosmarinic acid in these medicines from different sources exhibited great variations. Referring to the standards in Chinese Pharmacopoeia (CP), European Pharmacopeia (EP), and United States Pharmacopeia (USP), Non-Danshen from S. brachyloma, S. castanea, S. trijuga, S. bowleyana, and S. przewalskii were assessed as unqualified, and Danshen in the Shandong Province had the best quality due to the high qualified rate. Based on random forest (RF) and partial least-squares discriminant analysis (PLS-DA), NIR technique could successfully monitor the quality of these medicines by discriminating the species and regions with the accuracies of 100.00 and 99.60%, respectively. Additionally, modified partial least-squares regression (MPLSR) models were successfully constructed to investigate the feasibility of NIR fingerprints for the prediction of the quality indicators in these medicines. The optimized models obtained the best results for the total of tanshinone IIA, tanshinone I, and cryptotanshinone (TTC), tanshinone IIA, and salvianolic acid B, with the relative prediction deviation (RPD) of 4.08, 3.92, and 2.46, respectively. In summary, this study demonstrated that HPLC-DAD and NIR techniques can complement each other and could be simultaneously applied for evaluating and monitoring the quality of Danshen medicines.

Determination of some edible oils adulteration with paraffin oil using infrared spectroscopy

The spectroscopy of molecular vibrations using mid-infrared or near-infrared techniques was used more and more to characterize different compounds, including edible oil, in order to monitor any changes and to detect fraudulent modifications. This article presents a new method for quantitative adulteration of extra virgin olive oil (EVOO) or corn germ oil (CGO) with a mineral oil, such as paraffin oil (PO). A Fourier transform infrared (FT-IR) spectrometric method, using ATR spectra, was developed for the rapid, direct measurement of edible oils adulteration. The results indicate the efficiency of the proposed method for the detection of paraffin oil in adulteration of EVOO and CGO with RSD (< 3.0%). Graphical abstract:

Non-destructive testing of metal archaeological relics on the example of zinc dog tags discovered during the exhumation of people murdered in the Remand Prison at 37 Rakowiecka Street in Warsaw

The paper presents investigations of two partially damaged military dog tags. One of them is in relatively good condition while the second one is strongly damaged. Investigations focused on the better preserved military dog tag. Some of the characters were illegible. Thus the aim of the investigations was to identify the originally stamped characters using the multispectral method. The idea was to investigate objects not only in the visible part of the electromagnetic spectrum but also in other ranges, such as the near-infrared or the long-wave infrared spectral range. The research made use of dynamic infrared thermography, near-infrared techniques (with additional excitation by a near-infrared source) and visible techniques.

Classification of waste cotton from different countries using the near-infrared technique

Cotton fiber is mainly composed of cellulose, which is rarely separated into different kinds. However, the classification and identification of waste cotton from different countries are essential for the customs service of the country. In this study, the near-infrared classification method was introduced to classify and identify cotton fibers. Waste cotton samples from six different countries were collected, and one-fifth of them were used for validation. The near-infrared calibration and prediction models were constructed using both soft independent modeling of class analogy and partial least squares methods. It was found that the optimized model has a high recognition rate, and the prediction accuracy of the model was 99% for six countries. It was demonstrated that the near-infrared model established in this study can be used for fast and accurate identification of waste cotton from different countries.