Quality Evaluation of Shelled and Unshelled Macadamia Nuts by Means of Near‐Infrared Spectroscopy (NIR)

The quality of shelled and unshelled macadamia nuts was assessed by means of Fourier transformed near‐infrared (FT‐NIR) spectroscopy. Shelled macadamia nuts were sorted as sound nuts; nuts infected by Ecdytolopha aurantiana and Leucopteara coffeella; and cracked nuts caused by germination. Unshelled nuts were sorted as intact nuts (<10% half nuts, 2014); half nuts (March, 2013; November, 2013); and crushed nuts (2014). Peroxide value (PV) and acidity index (AI) were determined according to AOAC. PCA‐LDA shelled macadamia nuts classification resulted in 93.2% accurate classification. PLS PV prediction model resulted in a square error of prediction (SEP) of 3.45 meq/kg, and a prediction coefficient determination value (R_p²) of 0.72. The AI PLS prediction model was better (SEP = 0.14%, R_p² = 0.80). Although adequate classification was possible (93.2%), shelled nuts must not contain live insects, therefore the classification accuracy was not satisfactory. FT‐NIR spectroscopy can be successfully used to predict PV and AI in unshelled macadamia nuts, though.     

PREDICTION OF TEXTURE IN GREEN ASPARAGUS BY NEAR INFRARED SPECTROSCOPY (NIRS)

NIR spectroscopy was used to estimate three textural parameters of green asparagus: maximum cutting force, energy and toughness. An Instron 1140 Texturometer provided reference data. A total of 199 samples from two asparagus varieties (Taxara and UC‐157) were used to obtain the calibration models between the reference data and the NIR spectral data. Standard errors of cross validation (SECV) and r² were (5.73, 0.84) for maximum cutting force, (0.58, 0.66) for toughness, and (0.04, 0.85) for cutting energy. The mathematical models developed as calibration models were tested using independent validation samples (n =20); the resulting standard errors of prediction (SEP) and r² for the same parameters were (6.73, 0.82), (0.61, 0.57) and (0.04, 0.89), respectively. For toughness, substantially improved r² (0.85) and SEP (0.36) when four samples exhibiting large residual values were removed. The results indicated that NIRS could accurately predict texture parameters of green asparagus.     

Shortwave-near infrared spectroscopy for determination of reducing sugar content during grape ripening,winemaking, and aging of white and red wines

The feasibility of near infrared (NIR) spectroscopy for predicting reducing sugar content during grape ripening, winemaking, and aging was assessed. NIR calibration models were developed using a set of 146 samples scanned in a quartz flow cell with a 50 mm path length in the NIR region (800–1050 nm), in a fiber spectrometer system working in transmission mode. Principal component analysis (PCA), partial least squares (PLS), and multiple linear (MLR) regressions were used to interpret spectra and to develop calibrations for reducing sugar content in grape, must, and wine. The PLS model based on the full spectral range (800–1050 nm), yielded a determination coefficient (r²) of 0.98, a standard error of cross validation (SECV) of 13.62 g/l and a root mean square error of cross validation (RMSECV) of 13.58 g/l. The mathematical model was tested with independent validation samples (n = 48); the resulting values for r², the standard error of prediction (SEP) and the root mean square error of prediction (RMSEP) for the same parameter were 0.98, 10.84, and 12.20 g/l, respectively. The loading weights of latent variables from the PLS model were used to identify sensitive wavelengths. To assess their suitability, MLR models were built using these wavelengths. Wavelength significance was analyzed by ANOVA, and four wavelengths (909, 951, 961, and 975 nm) were selected, setting statistical significance at the 99% confidence level. The MLR model yielded acceptable results for r² (0.92), SEP (19.97 g/l) and RMSEP (20.51 g/l). The results suggest that NIR spectroscopy is a promising technique for predicting reducing sugar content during grape ripening, as well as during the fermentation and aging of white and red wines. Individual fingerprint wavelengths strongly associated with reducing sugar content could be used to enhance the efficacy of this simple, efficient and low-cost instrument.     

Preliminary study on the use of near‐infrared reflectance spectroscopy to assess nitrogen content of undried wheat plants

Near‐infrared reflectance (NIR) spectroscopy combined with chemometrics was used to assess nitrogen (N) and dry matter content (DM) and chlorophyll in whole‐wheat plant (Triticum aestivum L). Whole‐wheat plant samples (n = 245) were analysed by reference method and by visible and NIR spectroscopy, in fresh (n = 182) and dry (n = 63) presentations, respectively. Calibration equations were developed using partial least squares (PLS) and validated using full cross‐validation (leave‐one‐out method). Coefficient of determination in calibration (R²_CAL) and the standard error of cross‐validation (SECV) for N content in fresh sample presentation, after second derivative, were 0.89 (SECV: 0.64%), 0.86 (SECV: 0.66%) and 0.82 (SECV: 0.74%) using the visible + NIR, NIR and visible wavelength regions, respectively. Dry sample presentation gave better R²_CAL and SECV for N compared with fresh presentation (R²_CAL > 0.90, SECV < 0.20%) using visible + NIR. The results demonstrated that NIR is a suitable method to assess N concentration in wheat plant using fresh samples (unground and undried). Copyright © 2006 Society of Chemical Industry     

Enhanced NIR Calibration for Wort Fermentability Using Orthogonal Signal Correction

An enhanced method for the calibration of Near Infra Red (NIR) reflectance spectra to wort fermentability is proposed using a signal pre‐processing algorithm called orthogonal signal correction (OSC). Pre‐processing NIR spectra prior to partial least squares Project to Latent Structures (PLS) regression modelling is becoming commonplace in multivariate calibration. A set of twenty wort samples subjected to a replicated 2² factorial design with a centre point and nine production samples were used to construct multivariate prediction models. The experimental design factors were the mash tun saccharification temperature and time used to purposely provide a sample set with significant leverage in the fermentability responses. Calibration PLS models for both wort apparent degree of fermentation (ADF) and final attenuation apparent extract (Final AE) values with and without OSC corrected spectra were compared demonstrating significant improvements in prediction capability with the prior (Q² = 0.90 versus Q² = 0.28). The OSC algorithm removed almost 60% of the variance in the NIR spectra, which was independent or orthogonal to the fermentability measures. By cleaning up the spectra, the standard errors of prediction (SEP) for ADF and Final AE were improved by 50 and 90%, respectively, illustrating not only the enhancement in calibration but also the aptness for process control applications. Various model validation tests, including an external validation example and random response permutation, verify the validity of the models using OSC. Furthermore, interpretation of the important wavelengths related to wort fermentability is provided and demonstrates that some key wavelengths are related to both carbohydrate overtones as well as nitrogen functional groups. The application of OSC prior to developing calibration models with NIR demonstrates promising results for brewers interested in real time control of wort fermentability.     

Quantification of Fishmeal in Compound Feed Using NIR Spectroscopy

Analysis of fishmeal concentrations in feedstuff is critical to quality control and ingredient statement of commercial feed. Near-infrared reflectance spectroscopy (NIRS) determination for fishmeal was established through the spectral data processed by mean center, normalization, Savitzky–Golay first, partial least square 1 and cross validation. The coefficient of determination (R ²) of NIRS calibration model, the standard error estimated by cross validation (SECV) were 0.9554 and 0.9541, respectively. At the same time, the coefficient of determination of validation was 0.9867. Moreover, it has been proven to be directly correlated between the fishmeal spectra and the loading of the second principal components, and the coefficient of determination of the spectra of the calibration model. These results support our theory that the NIRS method constructed in our study could be used to quantify fishmeal in compound feeds.     

Non-destructive measurement of firmness and soluble solids content in bell pepper using NIR spectroscopy

The potential of near infrared (NIR) reflectance spectroscopy over the range 780–1690 nm was investigated to measure the soluble solids content (SSC) and firmness of bell pepper fruit. Partial least squares (PLS) calibration models were constructed based on a calibration dataset which included data from two cultivars (Solution and Ferrari) and two harvest times (2005 and 2006). The effect of Savitzky–Golay second derivative preprocessing and extended multiplicative signal correction (EMSC) on the accuracy of the calibration models was investigated and the best results were obtained with the former. The SECV were equal to 5.9 N and 0.59 °Brix for firmness and SSC, respectively. When the model was applied to an external data set including data from cv. Solution and a different harvest season, the satisfactory SEP values of 4.49 N and 0.7 °Brix were obtained, but for firmness a bias of 5.6 N was observed. From these results it can be concluded that NIR spectroscopy can be used as a non-destructive technique for measuring the SSC in bell pepper, but that further research is needed to make it robust for firmness prediction.     

Nondestructive estimation of maturity and textural properties on tomato ‘Momotaro’ by near infrared spectroscopy

Near infrared spectroscopy offers the possibility to classify and predict the internal quality of fruits and vegetables. The objective of this study was to evaluate the ability of near infrared spectroscopy to classify the maturity level and to predict textural properties of tomatoes variety “Momotaro”. Principal component analysis (PCA) and Soft independent modeling of class analogy (SIMCA) were used to distinguish among different maturities (mature green, pink and red). Partial least squares (PLS) regression was used to estimate textural properties, alcohol insoluble solids and soluble solids content of the tomatoes. The PCA calibration model with mean normalization pretreatment spectra of mature green tomatoes, gave the highest distinguishability (96.85%). It could classify 100.00% of red and pink tomatoes. The SIMCA model could not give better accuracy in maturity classification than individual PCA models. Among the textural parameters measured, the bioyield force from the puncture test with the near infrared (NIR) spectra (between 1100 and 1800 nm) pretreated by multiplicative scatter correction (MSC) had the highest correlation coefficient between NIR predicted and reference values (r = 0.95) and lowest standard error of prediction (SEP = 0.35 N) and bias of 0.19 N. The ratio of standard deviation of reference data of prediction set to standard error of prediction (RPD) was 2.71. In the case of Momotaro tomato, NIR spectroscopy by using PLS regression could not predict alcohol insoluble solids in fresh weight accurately but could predict soluble solids content well with r of 0.80, SEP of 0.210 %Brix and bias of 0.022 %Brix.     

Shedding light on human tissue (in vivo) to predict satiation,satiety, and food intake using near infrared reflectance spectroscopy: A preliminary study

The aim of this study was to evaluate the ability of a portable near infrared (NIR) instrument to collect the spectra in vivo of different tissues in healthy individuals and to relate their spectral information with food and energy intake, satiation, and satiety data. In this study, a hand-held NIR instrument was used to collect the spectra of different human tissues (e.g. arm, ear, face, jaw and wrist) with partial least squares (PLS) regression used to relate the NIR data with food and energy intake, satiation, and satiety measured in healthy individuals. The coefficient of determination in cross-validation (R²_CV) and the standard error in cross validation (SECV) for the prediction of satiety ranged between 0.58 and 0.62 and 223.3–235.0 total area under the curve (AUC), respectively, depending on the tissue analysed. The PLS cross-validation models based on the NIR spectra collected in both the arm and face tissues gave the best prediction of food intake (R² _CV 0.47–0.51, SECV 110.8-115 g). No workable calibrations were developed for the prediction of satiation, which might be associated with the inherent complexity of this parameter as well as the experimental conditions used to collect the data (e.g. type of tissue analysed). These results demonstrated the potential ability of in vivo NIR spectroscopy to identify tissue differences associated with satiety and food intake in individuals. However, a wider variety of food types, diets, and human subjects (samples) are needed to develop robust relationships between the NIR spectra of a tissue with both satiety and food intake.     

Quantification of glucose,fructose and sucrose in bayberry juice by NIR and PLS

Bayberry plays an important role in the nutrition and is a very important fruit-product. It has a high economic and officinal value. In this study, glucose, fructose and sucrose in bayberry juice were detected and quantified using near-infrared (NIR) spectroscopy. The HPLC method was assumed to provide the reference value of the analyte for calibration. Partial least-squares regression (PLSR) was used to construct calibration models with different pre-processing methods. The number of PLS factors was optimised. The results show PLS models are good for predicting glucose, fructose and sucrose concentrations in bayberry juice, and their prediction accuracy can be improved by using derivative process with the exception of the glucose. The best models were mostly given by the second derivative processed spectra, especially for sucrose with the determination coefficient, R² of 0.9931. This demonstrates the potential of NIR spectroscopy to quickly detect these components simultaneously in bayberry juice with the reference method of HPLC.     

Application of Portable and Handheld Infrared Spectrometers for Determination of Sucrose Levels in Infant Cereals

Sucrose coating of breakfast cereals is used to enhance the flavor and attractiveness of the final product but there is a need for monitoring its levels to meet consumer health concerns associated with sugar consumption. Our objective was to evaluate the use of portable (mid-infrared, MIR) and handheld (near-infrared, NIR) systems for rapid, simple and reliable determination of sucrose content in breakfast cereal products. Cereal-based and sucrose-coated samples were provided by an Ohio snack food company. Samples were ground and spectra were collected using portable ATR-MIR (Cary 630) and handheld NIR (microPHAZIR) spectrometers. Reference sucrose levels were determined by high-performance liquid chromatography (HPLC). Partial least squares regression (PLSR) was used to develop calibration regression models for prediction of sucrose levels in breakfast cereals based on spectral data. Sucrose levels in uncoated (n?=?28) and coated (n?=?62) cereal samples were on average of 1.2?±?0.7 and 11.8?±?3.5 g/100 g, respectively. Similar calibration (n?=?85) model performances were obtained for determination of sucrose content by using the portable MIR and handheld NIR instruments with standard error of cross-validation (SECV) of 1.45 %. However, superior predictive ability was obtained with the portable MIR unit using a validation set (n?=?20, SEP?=?1.27 % and RPD?=?4.41). Regression models using NIR spectrum of the cereal through a polyethylene bag resulted in reduction of the model goodness of fit and RPD values. Results support the application of handheld NIR and portable MIR spectrometers for close-to-real-time analysis of sucrose levels in breakfast cereals providing simple, rapid and reliable prediction for quality assurance.     

近红外透射法快速测定大豆油中的含磷量

采用近红外光谱仪,通过光学处理、数据处理和改进偏最小二乘法(MPLS)建立了快速测定高含磷量(321～632 mg/kg)和低含磷量(0～297 mg/kg)大豆油的近红外(NIR)模型。结果表明:高含磷量和低含磷量大豆油定标方程的交互定标决定系数(1-VR)分别为0.988和0.974,定标决定系数(R2)分别为0.992和0.980,定标标准误差(SEC)分别为2.420和2.512,交互定标标准误差(SECV)分别为2.538和2.678;现有数据预测标准偏差(SEP)分别为2.602和2.683;该近红外法在生产加工过程中可快速准确检测大豆油中含磷量。     

The Effect of Path Length on the Measurement Accuracies of Wine Chemical Parameters by UV,Visible, and Near-Infrared Spectroscopy

The use of spectral measurements using either UV, visible (VIS), or near-infrared (NIR) spectroscopy to characterize wines or to predict wine chemical composition has been extensively reported. However, little is known about the effect of path length on the UV, VIS, and NIR spectrum of wine and the subsequent effect on the performance of calibrations used to measure chemical composition. Several parameters influence the spectra of organic molecules in the NIR region, with path length and temperature being one of the most important factors affecting the intensity of the absorptions. In this study, the effect of path length on the standard error of UV, VIS, and NIR calibration models to predict phenolic compounds was evaluated. Nineteen red and 13 white wines were analyzed in the UV, VIS, and NIR regions (200–2500 nm) in transmission mode using two effective path lengths 0.1 and 1 mm. Principal component analysis (PCA) and partial least squares (PLS) regression models were developed using full cross validation (leave-one-out). These models were used to interpret the spectra and to develop calibrations for phenolic compounds. These results indicated that path length has an effect on the standard error of cross validation (SECV) absolute values obtained for the PLS calibration models used to predict phenolic compounds in both red and white wines. However, no statistically significant differences were observed (p > 0.05). The practical implication of this study was that the path length of scanning for wines has an effect on the calibration accuracies; however, they are non-statistically different. Main differences were observed in the PCA score plot. Overall, well-defined protocols need to be defined for routine use of these methods in research and by the industry.     

Changes in chemical quality indices during long‐term storage of palm‐olein oil under heated storage and transport‐type conditions

Six quality indices, namely free fatty acids (FFA), peroxide value (PV), anisidine value (AV), oxidative stability index (OSI), total tocopherols and headspace volatiles (hexanal, t‐2‐hexenal and t,t‐2,4‐decadienal), were evaluated in a long‐term storage trial of 52 weeks at 50 °C of palm‐olein, a monounsaturated oil. Three concentrations of copper (0.035, 0.17 and 0.69 mg kg^?1) were added. FFA values for all the sample treatments increased slightly over the storage period but remained within acceptable limits. PV of the copper‐containing samples declined initially and then remained stable up to week 40, after which it increased slightly for the 0.035 and 0.17 mg kg^?1 samples. However, PV of the control (no added copper) increased steadily to above the acceptable limit. AV of the copper‐containing samples increased much more than that of the control. OSI and total tocopherol values of the copper‐containing samples were markedly lower than those of the control. t‐2‐Hexenal did not increase during the storage period, whereas hexanal increased in the copper‐containing samples but at a slower rate than in the control. Conversely, the copper‐containing samples had high levels of t,t‐2,4‐decadienal but the control had none. AV, OSI and total tocopherols are the most valuable quality indices for assessing monounsaturated oil quality, whereas FFA, PV and headspace volatiles can be misleading. Copyright © 2003 Society of Chemical Industry     

Feasibility study of quantifying and discriminating soybean oil adulteration in camellia oils by attenuated total reflectance MIR and fiber optic diffuse reflectance NIR

Camellia oil is often the target for adulteration or mislabeling in China because of it is a high priced product with high nutritional and medical values. In this study, the use of attenuated total reflectance infrared spectroscopy (MIR-ATR) and fiber optic diffuse reflectance near infrared spectroscopy (FODR-NIR) as rapid and cost-efficient classification and quantification techniques for the authentication of camellia oils have been preliminarily investigated. MIR spectra in the range of 4000–650 cm⁻¹ and NIR spectra in the range of 10,000–4000 cm⁻¹ were recorded for pure camellia oils and camellia oil samples adulterated with varying concentrations of soybean oil (5–25% adulterations in the weight of camellia oil). Identifications is successfully made base on the slightly difference in raw spectra in the MIR ranges of 1132–885 cm⁻¹ and NIR ranges of 6200–5400 cm⁻¹ between the pure camellia oil and those adulterated with soybean oil with soft independent modeling of class analogy (SIMCA) pattern recognition technique. Such differences reflect the compositional difference between the two oils with oleic acid being the main ingredient in camellia oil and linoleic acid in the soybean oil. Furthermore, a partial least squares (PLS) model was established to predict the concentration of the adulterant. Models constructed using first derivative by combination of standard normal variate (SNV), variance scaling (VS), mean centering (MC) and Norris derivative (ND) smoothing pretreatments yielded the best prediction results With MIR techniques. The R value for PLS model is 0.994.The root mean standard error of the calibration set (RMSEC) is 0.645, the root mean standard error of prediction set (RMSEP) and the root mean standard error of cross validation (RMSECV) are 0.667 and 0.85, respectively. While with NIR techniques, NIR data without derivative gave the best quantification results. The R value for NIR PLS model is 0.992. The RMSEC, RMSEP and RMSECV are 0.70, 1.78 and 1.79, respectively. Overall, either of the spectral method is easy to perform and expedient, avoiding problems associated with sample handling and pretreatment than the conventional technique.     

Proximate analysis of homogenized and minced mass of pork sausages by NIRS

Near infrared spectroscopy was employed to analyse samples of pork sausage meat used in the manufacturing of typical Spanish sausages (minced and homogenized product). As well, two modes of analysis for the instrument were compared. Data from proximate analysis (fat, moisture and protein) were put into a calibration model by a diode array NIR spectrometer. The spectral range used was 515–1650 nm and different mathematical pre-treatments on the signal (derivatives and scatter corrections) were also compared. Different mathematical pre-treatments caused considerable changes in the statistics of the models (coefficients of determination and standard errors). R² (calibration) and standard errors of prediction (SEP, external validation) in minced sausage meat for fat, moisture and protein were 0.98, 0.98 and 0.93 (R²) and 1.38%, 1%, 0.83% (SEP), respectively. These values in homogenised sausage meat for fat, moisture and protein were 0.99, 0.98 and 0.93 (R²), and 0.94%, 0.76% and 0.87% (SEP), respectively.     

Monitoring water uptake in whole barley (Hordeum vulgare L.) grain during steeping using near infrared reflectance spectroscopy

The aim of this study was to evaluate the use of near infrared reflectance (NIR) spectroscopy to monitor water uptake and steeping time in whole barley samples as a rapid and easy to use technique. Whole barley grain samples were steeped in water, and subsamples were analyzed for water uptake (gravimetric method) and using NIR spectroscopy. The spectra and the analytical data were used to develop partial least squares (PLS) calibrations to predict water uptake and steeping time. Cross validation models for water uptake and steeping time gave a coefficient of determination in cross validation (R²) and the standard error of prediction (SEP) of 0.90 (SEP = 5.36 g/100 g fw) and 0.92 (SEP = 3.93 h), respectively. This study showed that NIR spectroscopy combined with PLS regression showed promise as a rapid, non-destructive method to monitor and measure water uptake and steeping time in whole barley during soaking.     

Purifying salmon oil using adsorption,neutralization, and a combined neutralization and adsorption process

The performance of activated earth and/or chitosan as an adsorbent to remove free fatty acids (FFA) and peroxides from the unpurified salmon oil was evaluated. The unpurified salmon oil was purified using three methods included activated earth adsorption process, neutralization process, and combined neutralization and activated earth adsorption processes. The purified salmon oil samples were evaluated for free fatty acids (FFA), peroxide values (PV), minerals, color, tocopherols, moisture content, insoluble impurities, and water activity. Neither chitosan nor the activated earth adsorption process was effective in removing FFA from the salmon oil. Neutralized oil had a higher intraparticular diffusion coefficient than the unpurified salmon oil for adsorbing peroxides. FFA of unpurified salmon oil was 3.5% and was significantly reduced (P < 0.05) to 0.12% by neutralization. No significant reduction of tocopherols content of the oil was observed in any of the three purification processes. After the adsorption processes, PV of neutralized oil had decreased from 4.75 to 2.90 mmol/kg. All three purification processes increased the lightness (L^∗) and decreased the redness (a^∗) and reduced mineral, insoluble impurities, moisture content, and water activity of the salmon oil. This study demonstrated that the combined process was more effective in reducing FFA, peroxides, and moisture content than either the activated earth adsorption or neutralization purification process alone.     

Texture Prediction in Intact Green Asparagus by Near Infrared (NIR) Spectroscopy,Assaying Linear and Non-linear Regression Strategies

Texture-related parameters were assessed in intact green asparagus at harvest and during postharvest storage using near-infrared spectroscopy combined with MPLS and LOCAL algorithms. Three spectrophotometers were evaluated for this purpose: a monochromator (range, 400–2,500 nm), a diode-array Vis–NIR spectrophotometer (range, 400–1,700 nm), and a handheld micro-electro-mechanical system (MEMS) spectrophotometer (range, 1,600–2,400 nm). Three hundred green asparagus spears (cv. “Grande”) were used to obtain calibration models based on reference data and NIR data. Results for maximum shear force showed that LOCAL algorithm improved the predictive capacity of models constructed using all three NIRS instruments, increasing r ² by 24, 16, and 56 % and reducing the SEP(c) values by 11, 8, and 14 %, respectively. For cutting energy, the LOCAL also improved the predictive capacity of the models (r ² increased by 3 % for the monochromator and the diode-array instrument and by 6 % for the MEMS device; and the SEP(c) decreased by 3 % in the three instruments). It is worth noting that while the monochromator and diode-array instruments displayed similar predictive capacity for the parameters tested, the MEMS instrument achieved slightly poorer results but has clear advantages for the measurement of texture in intact asparagus, being economical, portable, and easy to use in situ.     

Near-infrared spectroscopy coupled chemometric algorithms for prediction of antioxidant activity of black goji berries (<Emphasis Type="Italic">Lycium ruthenicum</Emphasis> Murr.)

In this study, near-infrared (NIR) spectroscopy coupled with partial least-squares (PLS) regression and various efficient variable selection algorithms, synergy interval-PLS (Si-PLS), backward interval PLS (Bi-PLS) and genetic algorithm-PLS (GA-PLS) were applied comparatively for the prediction of antioxidant activity in black wolfberry (BW). The eight assays were used for quantification of antioxidant content. The developed models were assessed using correlation coefficients (R²) of the calibration (Cal.) and prediction (Pre.); root mean square error of prediction, RMSEP; standard Error of Cross-Validation, RMSECV and residual predictive deviation, RPD. The performance of the built model greatly improved by the application of Si-PLS, Bi-PLS and GA-PLS compared with full spectrum PLS. The R² values determined for calibration and prediction set ranged from 0.8479 to 0.9696 and 0.8401 to 0.9638, respectively. These findings revealed that NIR spectroscopy combined with chemometric algorithms can be used for quantification of antioxidant activity in BW samples.