A Fourier Transform Infrared Spectroscopy Method for Analysis of Palm Oil Adulterated with Lard in Pre-Fried French fries

Fourier transform infrared spectroscopy with attenuated total reflectance accessory was used to detect the presence of lard in French fries pre-fried in palm oil adulterated with lard. A Fourier transform infrared calibration model was obtained using partial least squares for prediction of lard in a blend mixture of lard and palm oil. The coefficient of determination (R²) of 0.9791 was obtained with 0.5% of detection limit. The error in calibration expressed with root mean square error of calibration was 0.979%. In addition, the error obtained during cross validation was 2.45%. A discriminant analysis test was able to distinguish between fries samples adulterated with lard and samples, which were pre-fried with palm oils. Fourier transform infrared spectroscopy is a fast and powerful technique for quantification of lard present in French fries.      

Fourier Transform Infrared Spectroscopy Combined with Multivariate Calibrations for the Authentication of Avocado Oil

Avocado oil is one of the functional oils having high quality and high price in the market. This oil shows many benefits for the human health and is applied in many cosmetic products. The authentication of avocado oil becomes very important due to the possible adulteration of avocado oil with other lower priced oils, such as palm oil and canola oil. In this study, Fourier transform infrared spectroscopy using attenuated total reflectance in combination with chemometrics techniques of partial least squares and principal component regression is implemented to construct the quantification and classification models of palm oil and canola oil in avocado oil. Partial least squares at the wavenumbers region of 1260–900 cm^–1 revealed the best calibration models, having the highest coefficient of determination (R² = 0.999) and the lowest root mean square error of calibration, 0.80%, and comparatively low root mean square error of prediction, 0.79%, for analysis of avocado oil in the mixture with palm oil. Meanwhile, the highest R², root mean square error of calibration, and root mean square error of prediction values obtained for avocado oil in the mixture with canola oil at frequency region of 3025–2850 and 1260–900 cm^–1 were 0.9995, 0.83, and 0.64%, respectively.     

Analysis of lard in meatball broth using Fourier transform infrared spectroscopy and chemometrics

Meatball is one of the favorite foods in Indonesia. For the economic reason (due to the price difference), the substitution of beef meat with pork can occur. In this study, FTIR spectroscopy in combination with chemometrics of partial least square (PLS) and principal component analysis (PCA) was used for analysis of pork fat (lard) in meatball broth. Lard in meatball broth was quantitatively determined at wavenumber region of 1018–1284 cm^− 1. The coefficient of determination (R²) and root mean square error of calibration (RMSEC) values obtained were 0.9975 and 1.34% (v/v), respectively. Furthermore, the classification of lard and beef fat in meatball broth as well as in commercial samples was performed at wavenumber region of 1200–1000 cm^− 1. The results showed that FTIR spectroscopy coupled with chemometrics can be used for quantitative analysis and classification of lard in meatball broth for Halal verification studies. The developed method is simple in operation, rapid and not involving extensive sample preparation.     

Authentication of Extra Virgin Olive Oil from Sesame Oil Using FTIR Spectroscopy and Gas Chromatography

The presence of sesame oil in extra virgin olive oil has been investigated using Fourier transform infrared spectroscopy and gas chromatography. Frequencies of 1207–1018, 1517–1222, and 3050–2927 cm^?1 were chosen for quantification of sesame oil in extra virgin olive oil. Using Fourier transform infrared normal spectra coupled with a partial least square model, the root mean standard error of calibration and root mean standard error of prediction obtained were relatively low, i.e., 0.331 and 1.01% (vol/vol), respectively. Using fatty acid profiles as determined by gas chromatography, the levels of palmitic and oleic acids were decreased linearly with R² of 0.969 and 0.934, meanwhile the levels of stearic and linoleic acids were increased with R² of 0.930 and 0.959, respectively, with the increasing levels of sesame oil. From level 10% sesame oil (vol/vol), all these fatty acids are significantly different (p < 0.05).     

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.     

Fuzzy and Conventional Modeling of Open Sun Drying Kinetics for Roasted Green Wheat

Roasted green wheat is a high-value product made by roasting green (under-mature) wheat. Due to its high initial moisture content, drying is needed to avoid fast deterioration. Open sun drying of this product is by far the most common practice due to its simplicity, effectiveness, and low cost. The assumption of open sun drying adequacy to prevent deterioration was tested in this study. The drying data were fitted into eight common thin-layer drying models. Goodness of fit for each model was evaluated using coefficient of determination (R²) and root mean square error. The two-term exponential model was found to best describe open sun drying of roasted green wheat with R², and root mean square error values of 0.988 and 0.038, respectively. A fuzzy model of open sun drying for roasted green wheat was also developed and compared with conventional modeling. The results showed a much better performance of fuzzy model compared to conventional models with a much lower value of root mean square error (1.2?×?10^?6). The effective diffusivity was also evaluated for roasted green wheat kernels and found to be 1.7?×?10^?11 m²/s, which was in agreement with published data. The results showed that open sun drying for 5.5 h was effective and adequate to reduce moisture content to a safe level and to prevent deterioration of this product. These findings will provide valuable information for the design of a commercial roasted green wheat solar drying system.     

Direct and Simultaneous Quantification of Tannin Mean Degree of Polymerization and Percentage of Galloylation in Grape Seeds Using Diffuse Reflectance Fourier Transform‐Infrared Spectroscopy

The direct and simultaneous quantitative determination of the mean degree of polymerization (mDP) and the degree of galloylation (%G) in grape seeds were quantified using diffuse reflectance infrared Fourier transform spectroscopy and partial least squares (PLS). The results were compared with those obtained using the conventional analysis employing phloroglucinolysis as pretreatment followed by high performance liquid chromatography‐UV and mass spectrometry detection. Infrared spectra were recorded in solid state samples after freeze drying. The 2nd derivative of the 1832 to 1416 and 918 to 739 cm^?1 spectral regions for the quantification of mDP, the 2nd derivative of the 1813 to 607 cm^?1 spectral region for the degree of %G determination and PLS regression were used. The determination coefficients (R²) of mDP and %G were 0.99 and 0.98, respectively. The corresponding values of the root‐mean‐square error of calibration were found 0.506 and 0.692, the root‐mean‐square error of cross validation 0.811 and 0.921, and the root‐mean‐square error of prediction 0.612 and 0.801. The proposed method in comparison with the conventional method is simpler, less time consuming, more economical, and requires reduced quantities of chemical reagents and fewer sample pretreatment steps. It could be a starting point for the design of more specific models according to the requirements of the wineries.     

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.     

Near-infrared reflectance spectroscopy for the prediction of chemical composition in walnut kernel

In the present work, 116 samples were collected and near-infrared reflectance spectroscopy prediction model for determination of moisture, protein, and fat contents of walnut meal were obtained and evaluated. All the samples were analyzed based on the chemical methods. Meanwhile, they were scanned to obtain their near-infrared reflectance spectrum in the wavelength range of 570–1840 nm. Several preprocess treatments including scattering pretreatments, mathematical pretreatments, and aggression methods were optimized to increase the accuracy of the calibration models according to the coefficient of determination for calibration (R_c²) and the cross-validation (one minus the variance ratio, 1-VR), and the standard error of calibration and cross-validation. The results showed modified partial least square loading was the better aggression method to predict the moisture, proteins, and fats in walnut kernel. The calibration equations obtained indicated that near-infrared reflectance spectroscopy had excellent predictive capacity for the three components with R_c² = 0.965, standard error of calibration = 0.052 for moisture, and R_c² = 0.967, standard error of calibration = 0.191 for proteins, and R_c² = 0.979, standard error of calibration = 0.171 for fats, respectively. The external validation further confirmed the robustness and reliability of the near-infrared reflectance spectroscopy prediction models with the correlation coefficient of actual and predicted values (R²) = 0.952, ratio of performance deviation = 4.14, the standard error of prediction =0.058 for moisture, and R² = 0.977, ratio of performance deviation = 5.55, standard error of prediction = 0.182 for proteins, and R² = 0.990, ratio of performance deviation = 8.64, standard error of prediction = 0.191 for fats, respectively. Near-infrared reflectance spectroscopy is a reliable technology to predict these constituents in walnuts.     

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.     

Prediction of Cadmium content in brown rice using near‐infrared spectroscopy and regression modelling techniques

The feasibility of prediction of cadmium (Cd) content in brown rice was investigated by near‐infrared spectroscopy (NIRS) and chemometrics techniques. Spectral pretreatment methods were discussed in detail. Synergy interval partial least squares (siPLS) algorithm was used to select the efficient combinations of spectral subintervals and wavenumbers during constructing the quantitative calibration model. The performance of the final model was evaluated by the use of root mean square error of cross‐validation (RMSECV), root mean square error of prediction (RMSEP) and correlation coefficients for calibration set and prediction set (R_c and R_p), respectively. The results showed that the optimum siPLS model was achieved when two spectral subinterval and fifty‐two variables were selected. The predicted result of the best model obtained was as follows: RMSECV = 0.232, R_c = 0.930, RMSEP = 0.250 and R_p = 0.915. Compared with PLS and interval PLS models, siPLS model was slightly better than those methods. These results indicate that it is feasible to predict and screen Cd content in brown rice using NIRS.     

3步混合变量选择策略在鸡肉近红外水分检测中的应用

基于光谱多元校正中有效变量选择的3步混合策略(初筛、精挑、细选),提出了间隔偏最小二乘(iPLS)、区间变量迭代空间收缩法(iVISSA)和迭代保留信息变量(IRIV)联用的特征变量选择方法,对生鲜鸡胸肉的近红外光谱进行特征波长选择,建立了鸡肉水分检测模型。结果表明,建模波长数量经iPLS-iVISSA-IRIV 3步选择后减少为全光谱建模的0.76%,但模型精确度和稳定性逐步提高。选定8个特征波长建模,其校正相关系数RC=0.907 7,校正均方根误差RMSEC=0.516 1;预测相关系数RP=0.943 5,预测均方根误差RMSEP=0.612 3。表明基于3步混合策略提出的iPLS-iVISSA-IRIV方法能有效选择鸡肉水分检测的特征波长。     

Raman spectroscopy based characterization of desi ghee obtained from buffalo and cow milk

Raman spectroscopy bands (1006, 1156 and 1520 cm⁻¹) representing beta-carotenoids differentiated cow ghee from buffalo ghee. The band at 1080 cm⁻¹ represented free cholesterol, the concentration of which was higher in cow ghee than in buffalo ghee. Vitamin D and conjugated linoleic acid (CLA) in both cow ghee and buffalo ghee was identified through their Raman bands; the former contained more CLA isomers. A partial least squares regression model was developed to predict cow ghee adulteration of buffalo ghee and unknown samples. Coefficient of determination, standard error of prediction and standard error of calibration values of 0.96, 0.101 and 0.105, respectively, confirmed the authenticity of the model. Unknown samples loaded into the model yielded values of 0 or 1, indicating pure buffalo ghee or cow ghee adulteration, respectively. Nine unknown samples were tested blind; the root mean square error in prediction was 0.02, confirming the accuracy of the model.     

Predicting the colour properties of viscose knitted fabrics using soft computing approaches

The aim of this paper was to predict the colour strength of viscose knitted fabrics by using fuzzy logic (FL) model based on dye concentration, salt concentration and alkali concentration as input variables. Moreover, the performance of fuzzy logic (FL) model is compared with that of artificial neural network (ANN) model. In addition, same parameters and data have been used in ANN model. From the experimental study, it was found that dye concentration has the main and greatest effects on the colour strength of viscose knitted fabrics. The coefficient of determination (R²), root mean square (RMS) and mean absolute errors (MAE) between the experimental colour strength and that predicted by FL model are found to be 0.977, 1.025 and 4.61%, respectively. Further, the coefficient of determination (R²), root mean square (RMS) and mean absolute errors (MAE) between the experimental colour strength and that predicted by ANN model are found to be 0.992, 0.726 and 3.28%, respectively. It was found that both ANN and FL models have ability and accuracy to predict the fabric colour strength effectively in non-linear domain. However, ANN prediction model shows higher prediction accuracy than that of Fuzzy model.     

基于便携式近红外光谱仪的黄桃腐败时间快速预测

目的：利用便携式近红外（near infrared, NIR）光谱仪与化学计量学方法预测黄桃的腐败时间。方法：利用便携式NIR光谱仪采集黄桃样本的漫反射光谱,通过光谱预处理方法提高数据特征,采用偏最小二乘法（partial least squares, PLS）建立黄桃腐败时间的预测模型。通过均方根误差（root mean square error, RMSE）和决定系数（coefficient of determination, R²）评估模型的预测效果。结果：模型对黄桃腐败时间预测的R²为0.63,RMSE为4.09 d。结论：NIR光谱结合化学计量学方法能够实现黄桃腐败时间的无损、准确预测。     

FTIR Spectroscopy Combined with Chemometric for Analysis of Sesame Oil Adulterated with Corn Oil

Sesame oil is an edible vegetable oil derived from the sesame seed that has been used as a flavor enhancer in Southeast Asian cuisine. This highly valuable oil can be subjected to adulterations with lower price oils in order to gain economical profit. Among 10 vegetable oils evaluated using fatty acid profiles with principal component analysis, corn oil has the closest similarity in fatty acids combined together with sesame oil; therefore, corn oil is a potential adulterant in sesame oil. FTIR spectra at 1072?935 cm^?1 was chosen for quantitative analysis with acceptable values of coefficient determination (R²), root mean square errors of calibration and prediction. These combined methods using first derivative FTIR spectra in partial least square showed well quantified corn oil in sesame oil with R² (0.992), root mean square errors of calibration (0.53% v/v) and root mean square errors of prediction (1.31% v/v) values. Moreover, the Coomans plot based on Mahalanobis distance were able to discriminate between sesame oil with adulterated oils such as corn oil, grape seed oil, and rice bran oil.     

Texture Quality Analysis of Rainbow Trout Using Hyperspectral Imaging Method

In this study a hyperspectral imaging system (short wave infrared range from 1000 to 2500 nm) was used to model fish texture by experimental compression test. Partial least square–discriminate analysis modeling technique was used for classifying the samples by linking the hyperspectral information and their measured texture. The R² of cross validation and prediction were 0.97 and 0.96, respectively. The root mean squared errors for cross validation and prediction were 0.07 and 0.09, respectively. Sensitivity and specificity for both class I and II were 1.00. Results indicated that hyperspectral imaging in short wave infrared range has ability to detect texture stiffness of rainbow trouts which is affected by freshness.     

A rapid spectroscopic method for quantification of P57 in Hoodia gordonii raw material

The feasibility of quantifying the perceived active ingredient (P57) in Hoodia gordonii raw material using Fourier transform near- and mid-infrared spectroscopy combined with chemometric techniques was investigated. The concentration of P57 (a triterpene glycoside) was determined in 146 plant samples with liquid chromatography coupled to mass spectrometry and these values were used to develop a calibration model based on the partial least squares projections to latent structures (PLS) and orthogonal projections to latent structures (O-PLS) regression algorithms. The performance of each calibration model was evaluated according to the root mean square error of prediction (RMSEP) and correlation coefficient (R²). The PLS model with 2nd derivative pre-processing predicted P57 content based on the FT-NIR spectra with the best accuracy and a correlation coefficient (R²) value of 0.9629 and the lowest RMSEP of 0.03%. These results demonstrated that FT-NIR spectroscopy can be used to rapidly quantify P57 in H. gordonii raw material with high accuracy.     

Predicting tenderness of fresh ovine semimembranosus using Raman spectroscopy

A hand held Raman probe was used to predict shear force (SF) of fresh lamb m. semimembranosus (topside). Eighty muscles were measured at 1 day PM and after a further 4 days ageing (5 days PM). At 1 day PM sarcomere length (SL) and particle size (PS) were measured and at 5 days PM, SF, PS, cooking loss (CL) and pH were also measured. SF values were regressed against Raman spectra using partial least squares regression and against traditional predictors (e.g. SL) using linear regression. The best prediction of SF used spectra at 1 day PM which gave a root mean square error of prediction (RMSEP) of 11.5 N (Null = 13.2) and the squared correlation between observed and cross validated predicted values (R²_cv) was 0.27. Prediction of SF based on the traditional predictors had smaller R² values than using Raman spectra justifying further study on Raman spectroscopy.     

Rapid and non-invasive analysis of deoxynivalenol in durum and common wheat by Fourier-Transform Near Infrared (FT-NIR) spectroscopy

Fourier transform near-infrared spectroscopy (FT-NIR) was used for rapid and non-invasive analysis of deoxynivalenol (DON) in durum and common wheat. The relevance of using ground wheat samples with a homogeneous particle size distribution to minimize measurement variations and avoid DON segregation among particles of different sizes was established. Calibration models for durum wheat, common wheat and durum + common wheat samples, with particle size <500 µm, were obtained by using partial least squares (PLS) regression with an external validation technique. Values of root mean square error of prediction (RMSEP, 306–379 µg kg^–1) were comparable and not too far from values of root mean square error of cross-validation (RMSECV, 470–555 µg kg^–1). Coefficients of determination (r ²) indicated an “approximate to good” level of prediction of the DON content by FT-NIR spectroscopy in the PLS calibration models (r ² = 0.71–0.83), and a “good” discrimination between low and high DON contents in the PLS validation models (r ² = 0.58–0.63). A “limited to good” practical utility of the models was ascertained by range error ratio (RER) values higher than 6. A qualitative model, based on 197 calibration samples, was developed to discriminate between blank and naturally contaminated wheat samples by setting a cut-off at 300 µg kg^–1 DON to separate the two classes. The model correctly classified 69% of the 65 validation samples with most misclassified samples (16 of 20) showing DON contamination levels quite close to the cut-off level. These findings suggest that FT-NIR analysis is suitable for the determination of DON in unprocessed wheat at levels far below the maximum permitted limits set by the European Commission.