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.     

Determination of fat in live farmed Atlantic salmon using non‐invasive NIR techniques

Non‐destructive near‐infrared (NIR) measurements were performed on 100 live, anaesthetised farmed Atlantic salmon, whole weight 1–11 kg, using two different NIR instruments: a grating monochromator instrument equipped with a fibre optic interactance probe, and a diode array instrument measuring diffuse reflectance in a non‐contact mode. Crude fat content was determined using partial least squares (PLS) regression. Full cross‐validation was used to evaluate the performance of the calibration models, expressed as the root mean square error of prediction (RMSEP). For the fibre optic instrument the wavelength range from 800 to 1098 nm resulted in a correlation coefficient of 0.90 and an RMSEP equal to 14 g kg^?1 fat. The diode array instrument using the wavelength range from 900 to 1700 nm gave results of the same accuracy. The measurement times were 21 and 3 s respectively. It is concluded that either instrument could be used to determine the crude fat content in live Atlantic salmon, with good accuracy. © 2003 Society of Chemical Industry     

A comparative study for the quantitative determination of soluble solids content,pH and firmness of pears by Vis/NIR spectroscopy

Visible and near infrared (Vis/NIR) spectroscopy was investigated to determine the soluble solids content (SSC), pH and firmness of different varieties of pears. Two-hundred forty samples (80 for each variety) were selected as sample set. Two-hundred ten pear samples (70 for each variety) were selected randomly for the calibration set, and the remaining 30 samples (10 for each variety) for the validation set. Partial least squares (PLS) and least squares-support vector machine (LS-SVM) with different spectral preprocessing techniques were implemented for calibration models. Different wavelength regions including Vis, NIR and Vis/NIR were compared. It indicated that Vis/NIR (400–1800 nm) was optimal for PLS and LS-SVM models. Then, LS-SVM models were developed with a grid search technique and RBF kernel function. All LS-SVM models outperformed PLS models. Next, effective wavelengths (EWs) were selected according to regression coefficients. The EW-LS-SVM models were developed and a good prediction precision and stability was achieved compared with PLS and LV-LS-SVM models. The correlation coefficient of prediction (r_p), root mean square error of prediction (RMSEP) and bias for the best prediction by EW-LS-SVM were 0.9164, 0.2506 and −0.0476 for SSC, 0.8809, 0.0579 and −0.0025 for pH, whereas 0.8912, 0.6247 and −0.2713 for firmness, respectively. The overall results indicated that the regression coefficient was an effective way for the selection of effective wavelengths. LS-SVM was superior to the conventional linear PLS method in predicting SSC, pH and firmness in pears. Therefore, non-linear models may be a better alternative to monitor internal quality of fruits. And the EW-LS-SVM could be very helpful for development of portable instrument or real-time monitoring of the quality of pears.     

Miniature handheld NIR sensor for the on-site non-destructive assessment of post-harvest quality and refrigerated storage behavior in plums

This study evaluated the feasibility of using a handheld micro-electro-mechanical system (MEMS) spectrometer working in the 1600–2400 nm range for the measurement of quality-related parameters (soluble solid content, firmness, variety and post-harvest storage duration under refrigeration) in intact plums. Spectroscopic measurements were also made for each fruit using a diode-array Vis–NIR spectrophotometer (400–1700 nm) for purposes of comparison. A total of 264 plums (Prunus salicina L.) cv. ‘Black Diamond’, ‘Golden Globe’, ‘Golden Japan’, ‘Fortune’, ‘Friar’ and ‘Santa Rosa’, received and stored at 0 °C and 95% RH for 9 days, were used to build calibration models using different spectral signal pre-treatments and the modified partial least squares regression method. The two NIR instruments evaluated provided good precision, although the diode-array instrument yielded slightly greater precision for soluble solid content; statistic values were r² = 0.73 and the standard error of cross validation (SECV) = 1.11% for calibration, and r² = 0.68 and the standard error of prediction (SEP) = 1.22% for validation. Firmness measurements were less precise in both instruments, though again slightly better in the diode-array instrument: r² = 0.64 and SECV = 1.77 N for calibration; and r² = 0.61 and SEP = 2.30 N for validation, respectively. The performance of the two instruments for classifying plums by variety and by refrigerated post-harvest storage duration (0, 6 and 9 days) was evaluated using partial least square-discriminant analysis. A total of 96.5 % of samples were correctly assigned to their variety, while 94.5 % of plums were correctly assigned to their refrigerated storage day. In general, promising results were obtained with both instruments, with similar levels of accuracy for the measurements for soluble solid content, variety and refrigerated storage duration; the prediction model developed using the diode-array spectrophotometer provided better results for firmness.     

Measurement of Soluble Solids Content and pH of Yogurt Using Visible/Near Infrared Spectroscopy and Chemometrics

Visible/near infrared spectroscopy (Vis/NIRs) technique was applied to non-destructive quantification of sugar and pH value in yogurt. Partial least squares (PLS) analysis and least squares support vector machine (LS-SVM) were implemented for calibration models. In this paper, three brands (Mengniu, Junyao, and Guangming) were set as the calibration, and the remaining two brands (Yili and Shuangfeng) were used as prediction set. In the LS-SVM model, the correlation coefficient (r), root mean square error of prediction, and bias in prediction set were 0.9427, 0.2621°Brix, 1.804e−09 for soluble solids content, and 0.9208, 0.0327, and 1.094e−09 for pH, respectively. The correlation spectra corresponding to the soluble solids content and pH value of yogurt were also analyzed through PLS method. LS-SVM model was better than PLS models for the measurements of soluble solids content and pH value. The results showed that the Vis/NIRs combined with LS-SVM models could predict the soluble solids content and pH value of yogurt.     

Determination of oil and water in olive and olive pomace by NIR and multivariate analysis

Fourier transform (FT) Near Infrared Spectroscopy (NIR) spectrometry in combination with partial least squares (PLS) regression was used for direct, reagent-free determination fat and moisture content in milled olive and olive pomace. The two calibration models obtained were built with samples from two years harvest (2006/2007) and have a good predictive power considering the nature of the samples and are both being used in an industrial plant.     

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.     

基于最小二乘支持向量机的白酒酒醅成分定量分析

利用近红外光谱技术实现对白酒发酵过程中酒醅主要成分的质量控制,并进行模型优化,提高性能。采用偏最小二乘法提取的潜在变量作为最小二乘支持向量机的输入变量,先后建立了白酒酒醅中酒精度、淀粉、水分、酸度的近红外定量模型,并与经无信息变量消除法波段筛选后建立的偏最小二乘模型结果进行比较。结果表明：与偏最小二乘模型相比,4 个指标的最小二乘支持向量机定量模型的相关系数（R2）、预测均方根误差以及相对分析误差3 个评价参数均有更优表现;对未知样品进行预测时,最小二乘支持向量机模型的预测准确度明显高于偏最小二乘模型。说明最小二乘支持向量机模型的准确度、稳定性及预测性能均优于偏最小二乘法模型,为白酒酒醅的品质分析方法研究提供了新的思路。     

Measurement of Soluble Solid Contents and pH of White Vinegars Using VIS/NIR Spectroscopy and Least Squares Support Vector Machine

Visible and near-infrared (VIS/NIR) spectroscopy combined with least squares support vector machine (LS-SVM) was employed to determine soluble solid contents (SSC) and pH of white vinegars. Three hundred twenty vinegar samples were distributed into a calibration set (240 samples) and a validation set (80 samples). Partial least squares (PLS) analysis was implemented for the regression model and extraction of latent variables (LVs). The selected LVs were used as LS-SVM input variables. Finally, LS-SVM models with radial basis function kernel were achieved with the comparison of PLS models. The results indicated that LS-SVM outperformed PLS models. The correlation coefficient (r), root mean square error of prediction, bias, and residual prediction deviation for the validation set were 0.988, 0.207°Brix, 0.183, and 6.4 for SSC whereas these were 0.988, 0.041, ?0.002, and 6.5 for pH, respectively. The overall results indicated that VIS/NIR spectroscopy and LS-SVM could be used as a rapid alternative method for the prediction of SSC and pH of white vinegars, and the results could be helpful for the fermentation process and quality control monitoring of white vinegar production.     

Estimation of olive oil acidity using FT-IR and partial least squares regression

Olive oil characteristics are directly related to olive quality. Information about olive quality is of paramount importance to olive and olive oil producers, in order to establish its price. Real-time characterization of the olives avoids mixtures of high quality with low quality fruits, and allows improvement of olive oil quality. This work describes an indirect determination of olive acidity and that allows a rapid evaluation of olive oil quality. The applied method combines chemical analysis (30 min Soxhlet olive pomace extraction) in tandem with a spectroscopic technique (FT-IR) and multivariate regression (PLS1). The most suitable calibration model found used SNV pre-processing and was built with 4 Latent Variables giving a RMSECV of 8.7% and a Q² of 0.97. This accurate calibration model allows the estimation of olive acidity using a FT-IR spectrum of the corresponding Soxhlet oil dry extract and therefore is a suitable method for indirect determination of FFA in olives.     

大米蛋白质、脂肪、总糖、水分近红外检测模型研究

采用近红外光谱技术对大米蛋白质、脂肪、总糖、含水量进行检测。运用经典Kennard-Stone法选取校正集及预测集样本,运用分段小波消噪对光谱进行预处理,通过竞争性自适应重加权采样筛选出与样本性质相关的特征波长,比较偏最小二乘法和BP神经网络法所建立的大米蛋白质、脂肪、总糖、含水量的检测模型。对于大米蛋白质、总糖和水分的检测,2种方法所建立模型的决定系数均大于0.9,相对标准差均小于2.6%,具有良好的精度和稳定性。对于大米脂肪的检测,偏最小二乘模型的性能相对稍好,其决定系数为0.949 5,相对标准差为13.69%。     

Applying Near-Infrared Spectroscopy and Chemometrics to Determine Total Amino Acids in Herbicide-Stressed Oilseed Rape Leaves

Near-infrared (NIR) spectroscopy was investigated to determine the total amino acids (TAA) in oilseed rape (Brassica napus L.) leaves under a new herbicide—propyl 4-(2-(4,6-dimethoxypyrimidin-2-yloxy)benzylamino)benzoate (ZJ0273)—stress. In full-spectrum partial least squares (PLS) models, direct orthogonal signal correction (DOSC) was the best preprocessing method. Successive projections algorithm (SPA) was used to select the relevant variables. Multiple linear regression (MLR), PLS, and least squares-support vector machine (LS-SVM) were used for calibration. The DOSC–SPA–LS-SVM model achieved the best prediction performance with correlation coefficients r = 0.9968 and root mean squares error of prediction (RMSEP) = 0.2950 comparing all SPA–MLR, SPA–PLS, and SPA–LS-SVM models. Some parsimonious direct functions were also developed based on the DOSC–SPA wavelength (1,340 nm) such as linear, index, logarithmic, binominal, and exponential functions. The best performance was achieved by direct exponential function with r = 0.9968 and RMSEP = 0.2943. The overall results indicated that NIR was able to determine the TAA in herbicide-stressed oilseed rape leaves, and the DOSC–SPA was quite helpful for the development of detection sensors and the monitoring of the growing status and herbicide effect on field crop oilseed rape.     

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.     

A feasibility study on on-line determination of rice wine composition by Vis–NIR spectroscopy and least-squares support vector machines

The feasibility of visible and near infrared (Vis–NIR) spectroscopy and least-squares support vector machines (LS-SVM) for on-line determination of rice wine composition was investigated. A circle-light fibre spectrometer system was designed to collect transreflectance spectra of rice wine samples in round brown glass bottles with the bottle sealed and the labels removed. Statistical equations were established between reference data and Vis–NIR spectra by LS-SVM. Compared to partial least squares regression (PLSR), the performance of LS-SVM was slightly better, with higher correlation coefficients for validation (r_val) of 0.915, 0.888 and 0.872, and lower root mean square error of validation (RMSEP) of 0.168 (%(V V⁻¹)), 0.146 (g L⁻¹) and 0.033 for alcohol content, titratable acidity, and pH, respectively. Based on the results, it was concluded that the Vis–NIR spectrometer system was suitable for on-line wine quality determination, and LS-SVM was a reliable multivariate method for NIR analysis.     

Prediction of titratable acidity, malic acid, and citric acid in bayberry fruit by near-infrared spectroscopy

An experiment was conducted to simultaneously measure titratable acidity, malic acid, and citric acid of bayberry fruit in a nondestructive manner using near-infrared (NIR) transmittance spectroscopy and chemometrics. The sampling set included different cultivars that were obtainable from different areas in China. Calibration models using partial least squares (PLS) regression were developed based on GB 12293-90 of China and with high-performance liquid chromatography (HPLC) as reference methods. Different preprocessing methods and different wave bands were applied. The correlation coefficient of calibration (r_c), root-mean-square error of calibration (RMSEC), and root-mean-square error of prediction (RMSEP) of the best model for titratable acidity was 0.8959, 2.24, and 2.89 g/L, respectively, with the range of 10,000-5405 cm⁻¹. R_c, RMSEC, and RMSEP values for malic acid and citric acid were 0.6689, 0.32, 0.47 and 0.8970, 1.51, 2.12 g/L, respectively. The prediction accuracies could not be improved by using first and second derivative pretreatment methods. Due to the short time consumption and low monitoring cost, NIR spectroscopic technique has its potential for the rapid and nondestructive prediction of titratable acidity and citric acid in bayberry fruit in a temperature-controlled room, although the accuracy was not high.     

Chromatographic Fingerprinting with Multivariate Data Analysis for Detection and Quantification of Apricot Kernel in Almond Powder

Adulteration of almond powder samples with apricot kernel was solved by gas chromatographic fatty acid fingerprinting combined with multivariate data analysis methods (principal component analysis (PCA), PCA-linear discriminant analysis (PCA-LDA), partial least squares (PLS), and LS support vector machine (LS-SVM). Different almond and apricot kernel samples were mixed at concentrations ranging from 10 to 90% w/w. PCA and PCA-LDA methods were applied for the classification of almonds, apricot kernels, and mixtures. PLS and LS-SVM were used for the quantification of adulteration ratios of almond. Models were developed using a training data set and evaluated using a validation data set. The root mean square error of prediction (RMSEP) and coefficient of determination (R ²) of validation data set obtained for PLS and LS-SVM were 5.01, 0.964 and 2.29, 0.995, respectively. The results showed that the methods can be applied as an effective and feasible method for testing almond adulteration.     

A feasibility study on quantitative analysis of glucose and fructose in lotus root powder by FT-NIR spectroscopy and chemometrics

The feasibility of rapid analysis of glucose and fructose in lotus root powder by Fourier transform near-infrared (FT-NIR) spectroscopy was studied. Diffuse reflectance spectra were collected between 4000 and 12,432 cm⁻¹. Calibration models established by partial least-squares regression (PLSR), interval PLS of forward (FiPLS) and backward (BiPLS), back propagation-artificial neural networks (BP-ANN) and least squares-support vector machine (LS-SVM) were compared. The optimal models for glucose and fructose were obtained by LS-SVM with the first 10 latent variables (LVs) as input. For fructose the correlation coefficients of calibration (r_c) and prediction (r_p), the root-mean-square errors of calibration (RMSEC) and prediction (RMSEP), and the residual predictive deviation (RPD) were 0.9827, 0.9765, 0.107%, 0.115% and 4.599, respectively. For glucose the indexes were 0.9243, 0.8286, 0.543%, 0.812% and 1.785. The results indicate that NIR spectroscopy technique with LS-SVM offers effective quantitative capability for glucose and fructose in lotus root powder.     

Postharvest shelf-life discrimination of nectarines produced under different irrigation strategies using NIR-spectroscopy

Quick assessment of storage time in fruits is important for both growers and consumers due to the fresh fruit market is becoming increasingly demanding with regard to product quality. This study sought to evaluate the ability of near-infrared reflectance spectroscopy (NIRS) to classify intact nectarines in post-harvest storage, as a function of pre-harvest irrigation strategies applied and post-harvest cold storage duration. A total of 220 nectarine fruits (Prunus persica (L.) Batsch cv. ‘Sweet Lady’) were sampled after 7, 14, 21 and 28 days of refrigerated storage (0 °C, 95% RH) and at commercial harvest time. Two commercially-available spectrophotometers were evaluated for this purpose: a handheld MEMS spectrophotometer of 1600-2400 nm and a diode-array Vis/NIR spectrophotometer of 400-1700 nm. Models developed using partial least squares 2-discriminant analysis (PLS2-DA) correctly classified between 86 and 96% of samples by post-harvest storage time using the handheld instrument, and between 66 and 89% in the case of the diode-array spectrophotometer. Classification models based on pre-harvest irrigation treatment classified 57-84% of the samples correctly, due to the similarity in physical-chemical properties of fruits in both irrigation strategies. These results showed that NIRS could be used to monitor changes in nectarine quality parameters during pre- and post-harvest as an essential tool for decision-making both in-field and on-line.     

Discrimination of olive oil adulterated with vegetable oils using dielectric spectroscopy

The study focused on application of dielectric spectroscopy to identify the adulteration of olive oil. The dielectric properties of binary mixture of oils were investigated in the frequency range of 101 Hz–1 MHz. A partial least squares (PLS) model was developed and used to verify the concentrations of the adulterant. Furthermore, the principal component analysis (PCA) was used to classify olive oil sample as distinct from other adulterants based on their dielectric spectra. The results showed that the dielectric spectra of binary mixture of olive oil spiked with other oils increased with increasing concentration of soy, corn, canola, sesame, and perilla oils from 0% to 100% (w/w) over the measured frequency range. PLS calibration model showed a good prediction capability for the concentrations of the adulterant. For olive oil adulterated with soy oil, the results showed that the RMS was 0.053, sd(RMS), 0.017 and Q² value was 0.967. PCA classification plots for all oil samples showed clear performance in the differentiation for the different concentrations of the adulterant. Each of the oil samples could be easily grouped in different clusters using dielectric spectra. From the results obtained in this research, dielectric spectroscopy could be used to discriminate the olive oil adulterated with the different types of the oils at levels of adulteration below 5%.     

Detection of Organic Acids and pH of Fruit Vinegars Using Near-Infrared Spectroscopy and Multivariate Calibration

Near-infrared (NIR) spectroscopy was investigated to determine the acetic, tartaric, formic acids and pH of fruit vinegars. Optimal partial least squares (PLS) models were developed with different preprocessing. Simultaneously, the performance of least squares-support vector machine (LS-SVM) models was compared with three kinds of inputs, including wavelet transform (WT), latent variables, and effective wavelengths (EWs). The results indicated that all LS-SVM models outperformed PLS models. The optimal correlation coefficient (r), root mean square error of prediction and bias for validation set were 0.9997, 0.3534, and −0.0110 for acetic acid by WT-LS-SVM; 0.9985, 0.1906, and 0.0025 for tartaric acid by WT-LS-SVM; 0.9987, 0.1734, and 0.0012 for formic acid by EW-LS-SVM; and 0.9996, 0.0842, and 0.0012 for pH by WT-LS-SVM, respectively. The results indicated that NIR spectroscopy (7,800–4,000 cm⁻¹) combined with LS-SVM could be utilized as a precision method for the determination of organic acids and pH of fruit vinegars.