Rapid Near Infrared Spectroscopic Method for the Detection of Spoilage in Rainbow Trout (Oncorhynchus mykiss) Fillet

The possibility of using visible and short‐wavelength near‐infrared (SW‐NIR: 600 to 1100 nm) spectroscopy to detect the onset of spoilage and to quantify microbial loads in rainbow trout (Oncorhynchus mykiss) was investigated. Spectra were acquired on the skin and flesh side of intact trout fillet portions and on minced trout muscle samples stored at 4 °C for up to 8 d or at room temperature (21 °C) for 24 h. Principal component analysis (PCA) and partial least squares (PLS) chemometric models were developed to predict the onset and degree of spoilage. PCA results showed clear segregation between the control (day 1) and the samples held 4 d or longer at 4 °C. Clear segregation was observed for samples stored 10 h or longer at 21 °C compared with the control (0 h), indicating that onset of spoilage could be detected with this method. Quantitative PLS prediction models for microbial loads were established. For trout fillets, 4 °C: R= 0.97, standard error of prediction (SEP) = 0.38 log colony‐forming units (CFU)/g (flesh side); R= 0.94, SEP = 0.53 log CFU/g (skin side); R= 0.82, SEP = 0.82 log CFU/g for minced fish held at 21 °C. These results indicate that SW‐NIR in combination with multivariate statistical methods can be used to detect and monitor the spoilage process in rainbow trout and quantify microbial loads rapidly and accurately.     

Multivariate determination of free fatty acids and moisture in fish oils by partial least-squares regression and near-infrared spectroscopy

The oxidative and hydrolytic degradation of lipids in fish oil was monitored using partial least-squares (PLS) regression and near-infrared reflectance (NIR) spectroscopy. One hundred and sixty (n=160) fish oil samples from a fishmeal factory were scanned in transflectance by an NIR monochromator instrument (1100–2500 nm). Calibration models were performed for free fatty acids (FFA), moisture (M), peroxide value (PV) and anisidine value (AV). Coefficients of determination in calibration (R²) and standard errors of cross validation (SECV) were 0.96 (SECV: 0.59) and 0.94 (SECV: 0.03) for FFA and M in g/kg, respectively. The accuracy of the NIR calibration models were tested using a validation set, yielding coefficients of correlation (r) and standard errors of prediction (SEP) of 0.98 (SEP: 0.50) and 0.80 (SEP: 0.05) for FFA and M in g/kg, respectively. Poor accuracy (R²<0.80) was obtained for the NIR calibration models developed for PV and AV. The paper demonstrates that fish oil hydrolytic degradation of lipids, which seriously affect oil use and storage under industrial conditions, can be successfully monitored using PLS regression and NIR spectroscopy by the fishmeal industry.     

CHANGE OF FAT CONTENT AND FATTY ACID COMPOSITION OF TURKISH HAZELNUTS (CORYLUS AVELLANA L.) DURING STORAGE

Hazelnuts (Corylus avellana L.) were collected from three different cultivars (Tombul, Palaz and Kal?nkara) at the harvest season of 1996. The dried nuts were stored shelled and unshelled in polyethylene bags at 21C and 60–65% relative humidity for 12 months. During storage, the total fat content increased, the palmitic and oleic acid content of the oil increased, linoleic acid, ranged from 12.41 to 10.35%. No significant differences were found for other fatty acids during storage. The effect of storage of shelled and unshelled hazelnuts on the total fat content was significant.     

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     

近红外光谱波段优化在东北松子蛋白质定量检测中的应用

为了探究一种快速、无损与简便的东北松子品质检测方法,近红外光谱技术被应用到东北松子蛋白质无损检测研究中。利用偏最小二乘法建立带壳松子和去壳松仁的蛋白质定量分析模型,采用求导、多元散射校正、变量标准化校正、矢量归一化预处理方法优化模型,利用反向间隔偏最小二乘法、无信息变量消除法选取特征波段,建立全波段和特征波段下的偏最小二乘蛋白质预测模型。结果表明,带壳松子光谱经矢量归一化预处理方法后构建的模型最优,松仁光谱经变量标准化校正预处理方法后构建的模型最优;波段筛选能够优化模型质量,其中反向间隔偏最小二乘法的筛选结果最优,其带壳松子和松仁蛋白质模型校正集相关系数分别为0.9056和0.9383,验证集均方根误差分别为0.6670和0.5761。由此可知,经过优化后,模型的预测性能得到了提高,为带壳松子和松仁的蛋白质在线检测提供了一定的参考价值。     

Detection of clenbuterol in beef meat,liver and kidney by mid‐infrared spectroscopy (FT‐Mid IR) and multivariate analysis

Mid‐infrared spectroscopy (FT‐Mid IR) coupled with multivariate analysis was used to predict clenbuterol in beef meat, liver and kidney. A SIMCA model was also developed to discriminate between pure (beef meat, liver and kidney) and spiked with clenbuterol samples (beef meat‐clenbuterol, liver‐clenbuterol and kidney‐clenbuterol). The best models to predict clenbuterol concentrations were obtained using the partial least squares algorithm (PLS) with a R² > 0.9 and SEC and standard error of prediction <0.296 and 0.324, respectively. The SIMCA model used to discriminate pure and spiked with clenbuterol samples showed 100% correct classification rate. Methods detection limit was 2 μg kg^?1. FT‐Mid IR coupled with chemometrics could be a simple and rapid screening tool for monitoring clenbuterol in beef meat, liver and kidney implicated in food poisoning. This method could be use for screening purposes.     

Determination of aflatoxin risk components for in-shell Brazil nuts

A study was conducted on the risk from aflatoxins associated with the kernels and shells of Brazil nuts. Samples were collected from processing plants in Amazonia, Brazil. A total of 54 test samples (40?kg) were taken from 13 in-shell Brazil nut lots ready for market. Each in-shell sample was shelled and the kernels and shells were sorted in five fractions: good kernels, rotten kernels, good shells with kernel residue, good shells without kernel residue, and rotten shells, and analysed for aflatoxins. The kernel?:?shell ratio mass (w/w) was 50.2/49.8%. The Brazil nut shell was found to be contaminated with aflatoxin. Rotten nuts were found to be a high-risk fraction for aflatoxin in in-shell Brazil nut lots. Rotten nuts contributed only 4.2% of the sample mass (kg), but contributed 76.6% of the total aflatoxin mass (µg) in the in-shell test sample. The highest correlations were found between the aflatoxin concentration in in-shell Brazil nuts samples and the aflatoxin concentration in all defective fractions (R ^2?=?0.97). The aflatoxin mass of all defective fractions (R ^2?=?0.90) as well as that of the rotten nut (R ^2?=?0.88) were also strongly correlated with the aflatoxin concentration of the in-shell test samples. Process factors of 0.17, 0.16 and 0.24 were respectively calculated to estimate the aflatoxin concentration in the good kernels (edible) and good nuts by measuring the aflatoxin concentration in the in-shell test sample and in all kernels, respectively.     

A Feasibility Study on Monitoring Residual Sugar and Alcohol Strength in Kiwi Wine Fermentation Using a Fiber‐Optic FT‐NIR Spectrometry and PLS Regression

This work aims to investigate the potential of fiber‐optic Fourier transform‐near‐infrared (FT‐NIR) spectrometry associated with chemometric analysis, which will be applied to monitor time‐related changes in residual sugar and alcohol strength during kiwi wine fermentation. NIR calibration models for residual sugar and alcohol strength during kiwi wine fermentation were established on the FT‐NIR spectra of 98 samples scanned in a fiber‐optic FT‐NIR spectrometer, and partial least squares regression method. The results showed that R² and root mean square error of cross‐validation could achieve 0.982 and 3.81 g/L for residual sugar, and 0.984 and 0.34% for alcohol strength, respectively. Furthermore, crucial process information on kiwi must and wine fermentations provided by fiber‐optic FT‐NIR spectrometry was found to agree with those obtained from traditional chemical methods, and therefore this fiber‐optic FT‐NIR spectrometry can be applied as an effective and suitable alternative for analyses and monitoring of those processes. The overall results suggested that fiber‐optic FT‐NIR spectrometry is a promising tool for monitoring and controlling the kiwi wine fermentation process.     

Simultaneous analysis of main catechins contents in green tea (Camellia sinensis (L.)) by Fourier transform near infrared reflectance (FT-NIR) spectroscopy

This paper reported the results of simultaneous analysis of main catechins (i.e., EGC, EC, EGCG and ECG) contents in green tea by the Fourier transform near infrared reflectance (FT-NIR) spectroscopy and the multivariate calibration. Partial least squares (PLS) algorithm was conducted on the calibration of regression model. The number of PLS factors and the spectral preprocessing methods were optimised simultaneously by cross-validation in the model calibration. The performance of the final model was evaluated according to root mean square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP) and correlation coefficient (R). The correlations coefficients (R) in the prediction set were achieved as follows: R = 0.9852 for EGC model, R = 0.9596 for EC model, R = 0.9760 for EGCG model and R = 0.9763 for ECG model. This work demonstrated that NIR spectroscopy with PLS algorithm could be used to analyse main catechins contents in green tea.     

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.     

Use of ATR-FTIR spectroscopy coupled with chemometrics for the authentication of avocado oil in ternary mixtures with sunflower and soybean oils

Avocado oil is a high-value and nutraceutical oil whose authentication is very important since the addition of low-cost oils could lower its beneficial properties. Mid-FTIR spectroscopy combined with chemometrics was used to detect and quantify adulteration of avocado oil with sunflower and soybean oils in a ternary mixture. Thirty-seven laboratory-prepared adulterated samples and 20 pure avocado oil samples were evaluated. The adulterated oil amount ranged from 2% to 50% (w/w) in avocado oil. A soft independent modelling class analogy (SIMCA) model was developed to discriminate between pure and adulterated samples. The model showed recognition and rejection rate of 100% and proper classification in external validation. A partial least square (PLS) algorithm was used to estimate the percentage of adulteration. The PLS model showed values of R² > 0.9961, standard errors of calibration (SEC) in the range of 0.3963–0.7881, standard errors of prediction (SEP estimated) between 0.6483 and 0.9707, and good prediction performances in external validation. The results showed that mid-FTIR spectroscopy could be an accurate and reliable technique for qualitative and quantitative analysis of avocado oil in ternary mixtures.     

Predicting of intramuscular fat content in pork using near infrared spectroscopy and multivariate analysis

The potential of near infrared (NIR) spectroscopy combined with chemometrics methods was studied to rapidly detect intramuscular fat (IMF) content in pork. Near infrared diffuse reflectance spectra were recorded both with an FT-NIR and a USB4000 spectrometer. The data analysis was compared on different sample preparation, spectral range and spectra pretreatment. According to calibration statistics, best calibration for IMF showed R²_cal of 0.94, R²_val of 0.92, RMSEC of 0.233, RMSEP of 0.462 and RPD of 2.29. The prediction of IMF content for minced samples was more accurate than that for intact samples. The spectra obtained using FT-NIR contained much information correlating to the IMF content than the Vis-NIR spectra of USB4000. The results showed that NIR spectroscopy technique can be used to determine the IMF content in pork as a rapid, convenient, and feasible analysis tool.     

Near-Infrared Spectroscopy for Classification of Oranges and Prediction of the Sugar Content

A nondestructive method for the classification of orange samples according to their growing conditions and geographic areas was developed using Vis/Near infrared spectroscopy. The results showed that the NIR spectra of the samples were moderately clustered in the principle component space and pattern recognition wavelet transform (WT) combined artificial neural network (BP-ANN) provided satisfactory classification results. Additionally, a partial least square (PLS) method was constructed to predict the sugar content of certain oranges. It showed excellent predictions of the sugar content of oranges, with standard error of prediction (SEP) values of 0.290 and 0.301 for Shatangju and Huangyanbendizao, respectively.     

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.     

Non-destructive determination of water-holding capacity in fresh beef by using NIR hyperspectral imaging

This study was carried out for post-mortem non-destructive prediction of water holding capacity (WHC) in fresh beef using near infrared (NIR) hyperspectral imaging. Hyperspectral images were acquired for different beef samples originated from different breeds and different muscles and their spectral signatures were extracted. Both principal component analysis (PCA) and partial least squares regression (PLSR) models were developed to obtain an overview of the systematic spectral variations and to correlate spectral data of beef samples to its real WHC estimated by drip loss method. Partial least squares modeling resulted in a coefficient of determination (R_CV²) of 0.89 and standard error estimated by cross validation (SECV) of 0.26%. The PLSR loadings showed that there are some important absorption peaks throughout the whole spectral range that had the greatest influence on the predictive models. Six wavelengths (940, 997, 1144, 1214, 1342, and 1443 nm) were then chosen as important wavelengths to build a new PLS prediction model. The new model led to a coefficient of determination (R_CV²) of 0.87 and standard error estimated by cross validation (SECV) of 0.28%. Image processing algorithm was then developed to transfer the predicting model to each pixel in the image for visualizing drip loss in all portions of the sample. The results showed that hyperspectral imaging has the potential to predict drip loss non-destructively in a reasonable accuracy and the results could be visualised for identification and classification of beef muscles in a simple way. In addition to realize the difference in WHC within one sample, it was possible to accentuate the difference in samples having different drip loss values.     

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.     

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.     

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.     

Protein content determination in Brassica oleracea species using FT‐NIR technique and PLS regression

Six fresh and one frozen vegetable cultivar groups possessing remarkably different morphology from the same Brassica oleracea species, including broccoli, Brussels sprouts, curly cabbage, white cabbage, red cabbage, cauliflower and white kohlrabi, were chosen to set up a Fourier transform near‐infrared spectroscopy (FT‐NIR)‐based method for the quantification of protein content. Sample preparation was based on lyophilisation and homogenisation. Calibration was set up with the help of the Kjeldahl method for the quantification of protein content in the range of 12.9–32.5 m/m%. Calibration model was developed using the spectral regions 1136–1334 and 1639–1836 nm, with partial least squares regression. This model was checked by cross‐validation. The performance of the final FT‐NIR estimation model was evaluated by root mean square of cross‐validation, root‐mean‐square error of estimation and the determination coefficient (R²). The final estimation function for the protein determination was characterised with the predictive error of 0.76 m/m% and R² value of 98.81.     

Use of Attenuated Total Reflectance Mid‐Infrared Spectroscopy for Rapid Prediction of Amino Acids in Chinese Rice Wine

The high content of amino acids of Chinese rice wine (CRW), especially essential amino acids makes it a food increasingly demanded by consumers. Rapid detection technique of amino acid content, which is an important quality and function index of CRW, is highly desirable for consumers, producers as well as administrative authorities. In this study, the potential of Fourier transform infrared spectroscopy (FT‐IR) as a novel and rapid analytical technique to determine 17 free amino acids in CRW were investigated. Genetic algorithms (GA) and synergy interval partial least squares (SiPLS) were used to select the most efficient spectral variables to improve the prediction precision of the classic partial least squares (PLS) model constructed on the full‐spectrum. The results demonstrated that compared with the PLS model using all wavelengths of FT‐IR spectra, the prediction precision of model based on the spectral variables selected by GA and SiPLS was significantly improved, especially for arginine and proline. After systemic comparison and discussion, it was found that GA‐SiPLS model achieved the best performance, with the correlation coefficient in calibration (R² (cal)) higher than 0.80 and the residual predictive deviation higher than 2.00 for all of the free amino acids analyzed in this study. The overall results confirmed that FT‐IR combined with efficient variable selection algorithms is a method that may be useful to replace the traditional methods for routine analysis of free amino acids in CRW.