Non-destructive evaluation of maturity and quality parameters of pomegranate fruit by visible/near infrared spectroscopy

In this study, the potential of visible and near infrared spectroscopy was investigated to classify the maturity stage and to predict the quality attributes of pomegranate variety “Ashraf” such as total soluble solids content, pH, and titratable acidity during four distinct maturity stages between 88 and 143 days after full bloom. Principal component analysis was used to distinguish among different maturities. The prediction models of internal quality attributes of the pomegranate were developed by partial least squares regression. The transmission spectra of pomegranate were obtained in the wavelength range from 400 to 1100 nm. In this research several preprocessing methods were utilized including centering, smoothing (Savitzky–Golay algorithm, median filter), normalization (multiplicative scatter correction and standard normal variate) and differentiation (first derivative and second derivative). It concluded that different preprocessing techniques had effects on the classification performance of the model using the principal component analysis method. In general, standard normal variate and multiplicative scatter correction gave better results than the other pretreatments. The correlation coefficients (r), root mean square error of calibration and ratio performance deviation for the calibration models were calculated: r = 0.93, root mean square error of calibration = 0.22 °Brix and ratio performance deviation = 6.4 °Brix for total soluble solids; r = 0.84, root mean square error of calibration = 0.064 and ratio performance deviation = 4.95 for pH; r = 0.94, root mean square error of calibration = 0.25 and ratio performance deviation = 5.35 for titratable acidity.     

茶油掺伪定性鉴别模型的对比分析

为了解决茶油掺伪其他植物油的掺伪量定量预测问题,本研究基于14个特征性脂肪酸和甘油三酯指标,设置高/低两种不同掺伪梯度,运用Python语言构建并对比分析了偏最小二乘回归(PLSR)模型和多元线性回归(MLR)模型用于掺伪茶油掺伪量的定量预测的效果。研究表明,PLSR模型对掺伪茶油的定量预测效果不理想,高掺伪梯度下PLSR模型的平均RMSE值高达1.99,低掺伪梯度下PLSR模型的平均R²值(0.8888)较低,平均RMSE值(0.906 6)较高。除了对棕榈油掺伪量的定量预测效果较差外,在高/低掺伪梯度下MLR模型定量预测能力较强,平均R²值达到了0.999 873/0.993 572,平均RMSE值为0.146/0.136。结果表明MLR模型可用于不同掺伪质量分数和梯度下茶油掺伪不同食用植物油的掺伪量定量预测问题,效果较好。     

Non-destructive internal quality assessment of “Hayward” kiwifruit by waveguide spectroscopy

Soluble solids content (SSC) and Magness-Taylor flesh firmness (MTf) of “Hayward” kiwifruits were non-destructively assessed by means of a waveguide, that houses the fruit, connected to a sweeper oscillator and a spectrum analyzer. A preliminary test was conducted with a plastic fruit filled with solutions with different SSC values in the frequency range from 2 to 20 GHz (with a step of 1 GHz). The best linear correlations (R² up to 0.987) between electric signals and SSC solutions in the above described test were found in the 2-3 GHz and 15-16 GHz steps. These steps were used for the dielectric measurements on kiwifruit samples during storage of 28 days at 14 °C. Partial least squares (PLS) regression were then used to predict SSC and MTf from these acquired spectra. In “test set” validation, PLS models showed R² values up to 0.804 (RMSE = 0.98 °Brix) and 0.806 (RMSE = 8.9 N) for the prediction of SSC and MTf, respectively.     

Non-destructive and rapid analysis of chemical compositions in Thai steamed pork sausages by near-infrared spectroscopy

The objective of the present study was to evaluate the ability of near-infrared (NIR) spectroscopy to predict chemical compositions of Thai steamed pork sausages in relation to different types of sample presentation forms of NIR measurements (with and without plastic casing). NIR spectra of sausages were scanned to predict the chemical compositions, protein, fat, ash and carbohydrate non-destructively. NIR spectrum features of the sausage samples were strongly influenced by physical properties of the samples, such as the presence of plastic casing and inhomogeneous physical structure inside the samples, yielding significant baseline fluctuations. Thus, regression models were developed using partial least squares (PLS) regressions with two pretreatment methods, namely multiplicative scatter correction (MSC) and second derivative, which overcame the baseline problems. The prediction results suggest that the contents for the protein, fat and moisture can be estimated well with the proper selection of the pretreatment method.     

Quality control of beer using high-resolution nuclear magnetic resonance spectroscopy and multivariate analysis

High-resolution nuclear magnetic resonance (NMR) spectroscopy is introduced for the quality control and authenticity assessment of beer in official food control. Measurements were performed using a 400-MHz NMR spectrometer using flow injection technology for automatic sample changing. Only degassing and addition of buffer (pH 5.6 in D₂O for locking and 0.1% TSP for referencing) is required to prepare the beer samples. Differences in the spectral profiles of beers varying in type and origin were studied by principal component analysis (PCA), considering the spectrum to be a characteristic fingerprint. For the first time, the high throughput of a Flow-Injection NMR system allowed a comprehensive database of beer spectra for PCA classification to be established efficiently. Beers made with barley malt could be distinguished from those made with wheat malt. Clustering of beers from the same brewing sites was observed, as well as significant discrimination of beers with deteriorated quality. Using the partial least squares (PLS) method to correlate NMR spectra with results from reference methods, models for calculating the original gravity, ethanol and lactic acid were established. The results obtained suggest that NMR is a useful tool in the quality control of beer samples, since quantitative determination of essential compounds as well as chemometric classification are simultaneously possible. Compared to conventional methods, ¹H NMR spectroscopy is faster and requires simpler sample preparation.     

Variable selection for partial least squares analysis of soluble solids content in watermelon using near-infrared diffuse transmission technique

This work is focused on the variable selection in building the partial least squares (PLS) regression model of soluble solids content (SSC) that is used to evaluate quality grading of watermelon. The spectra were obtained by the near infrared (NIR) spectrometer with the device designed for on-line quality grading of watermelon and the spectra of 680–950 nm were adopted to analysis. The variable selection was based on Monte-Carlo uninformative variable elimination (MC-UVE) and genetic algorithm (GA). In comparison of the performances of the full-spectra (680–950 nm) PLS regression model and the feature wavelengths PLS regression model showed that the MC-UVE–GA–PLS model with baseline offset correction combined multiplicative scatter correction (MSC) pretreatment was much better and 14 variables in total were selected. The correlation coefficients between the predicted and actual SSC were 0.885 and 0.845, the root mean square errors were 0.562 °Brix and 0.574 °Brix for calibration and prediction set, respectively. This work can make a great contribution to the research of on-line quality grading for watermelon nondestructively.     

近红外光谱法测定面粉的水分、脂肪、碳水化合物和蛋白质含量

应用近红外光谱技术结合不同的定量分析方法建立面粉4种组分的快速定量模型。国标法测定68种面粉样品的水分、脂肪、碳水化合物和蛋白质的含量,并采集其近红外漫反射光谱图。选取58个校正集和10个验证集样品,通过马氏距离法剔除异常样品后,对比17种光谱预处理方式所建立的基于全光谱的偏最小二乘法(partial least squares,PLS)定量模型效果,在最佳预处理方法的基础上,采用向后区间偏最小二乘法(Backward interval PLS,BiPLS)筛选特征光谱,进一步得到最佳定量模型。结果表明,所建立的模型校正集相关系数Rcv均大于0.9650,内部交叉验证均方根误差均小于0.328;验证集相关系数均大于0.9926,预测均方根误差均低于0.383。因此,模型具有较好的准确性和稳定性,能应用于面粉的多指标快速检测。     

相关分析法在NIR检测桑蚕丝含量中的应用

研究采用近红外(NIR)光谱技术快速检测纺织原料中桑蚕丝含量的方法,在用偏最小二乘法(PLS)建立校正模型过程中,探讨互相关分析法对提高建模精度的作用。结果表明:混合数据经过相关分析法处理后,模型的预测精度有所提高,模型的平均绝对误差<2.5(标准差<1.5),测量值与含量参考值具有良好的相关性(相关系数0.996)。近红外光谱快速检测法可以满足桑蚕丝含量的实际测量要求,从而为纺织品的无损、快速检测提供一种新的方法。     

Application of Fourier transform near-infrared spectroscopy to optimization of green tea steaming process conditions

In this study, we constructed prediction models by metabolic fingerprinting of fresh green tea leaves using Fourier transform near-infrared (FT-NIR) spectroscopy and partial least squares (PLS) regression analysis to objectively optimize of the steaming process conditions in green tea manufacture. The steaming process is the most important step for manufacturing high quality green tea products. However, the parameter setting of the steamer is currently determined subjectively by the manufacturer. Therefore, a simple and robust system that can be used to objectively set the steaming process parameters is necessary. We focused on FT-NIR spectroscopy because of its simple operation, quick measurement, and low running costs. After removal of noise in the spectral data by principal component analysis (PCA), PLS regression analysis was performed using spectral information as independent variables, and the steaming parameters set by experienced manufacturers as dependent variables. The prediction models were successfully constructed with satisfactory accuracy. Moreover, the results of the demonstrated experiment suggested that the green tea steaming process parameters could be predicted on a larger manufacturing scale. This technique will contribute to improvement of the quality and productivity of green tea because it can objectively optimize the complicated green tea steaming process and will be suitable for practical use in green tea manufacture.     

Effect of fermented soybean product (Cheonggukjang) intake on metabolic parameters in mice fed a high‐fat diet

As a nontargeted metabolomics approach, we investigated changes in the plasma metabolite levels in a mouse model of obesity induced by a high‐fat diet and fermented soybean product diet. We analyzed the plasma samples by using ultra‐performance liquid chromatography coupled with quadrupole time‐of‐flight mass spectrometry (UPLC‐Q‐TOF‐MS). In the present study, the animals were divided into four groups according to the diet type; normal fat diet control group (ND), high‐fat diet control group (HD), high‐fat diet plus 30% cooked soybean power (HD + S), and high‐fat diet plus 30% 72‐h fermented Cheonggukjang powder (HD + CGJ). To examine the changes in plasma metabolite levels because of high‐fat diet feeding, total cholesterol and triglyceride levels were measured. Total cholesterol and triglyceride levels were lower in the HD + S and HD + CGJ groups than in the ND group. According to partial least‐squares discriminant analysis (PLS‐DA), major metabolites contributing to the discrimination between each group were assigned as lipid metabolites in plasma, e.g., lyso‐phosphatidylcholines and phosphatidylcholines. Therefore, diets containing soy‐based food products, which are rich sources of isoflavonoids, might be helpful for controlling the lipid metabolism under high‐fat diet conditions.     

Direct determination of polymerised triacylglycerides in deep-frying vegetable oil by near infrared spectroscopy using Partial Least Squares regression

A green method for the determination of polymerised triacylglyceride (PTG) in deep-frying vegetable oils of different botanic origin has been developed employing near infrared (NIR) spectroscopy and Partial Least Squares (PLS) regression. Four different types of oil were heated during several hours, with and without the addition of foodstuff. NIR transmission spectra were obtained directly from sample aliquots stored in glass vials, thus avoiding the consumption of solvents and minimising waste generation. Variables employed for building the PLS models were selected applying interval PLS (iPLS) as well as Uninformative Variable Elimination-PLS (UVE-PLS). A global PLS model using spectra of all four types of oils was compared to PLS models established for each oil type. Due to the small differences observed in the NIR spectra that can be related to the different botanic origin and results obtained from the PLS model comparison, the use of a global PLS model is recommended leading to prediction errors of 2.28% (w/w) for the determination of PTG in oils employed for frying different kinds of foods.     

基于近红外光谱快速定量检测面粉中曲酸的方法建立

利用近红外光谱技术快速定量检测面粉中非法添加的褐变抑制剂曲酸。选取市场上常见3?种基本类型的面粉（高、中、低筋面粉）,分别制备曲酸质量分数为0.0%、0.5%、1.0%、3.0%、5.0%、10.0%的面粉样品,并采集其在1?000～2?400?nm波段下的光谱数据。对比不同预处理下高筋面粉样品数据所建偏最小二乘（partial least squares,PLS）回归模型效果,选取Savitzky-Golay一阶导数为最优预处理方法。采用区间偏最小二乘（interval partial least squares,iPLS）法选取1?088.8～1?153.5?nm为最佳光谱区间。结果表明,基于最佳光谱区间所建PLS回归模型预测效果优于基于全波段光谱数据所建模型。进一步,基于所选最优区间对中、低筋面粉和混合样品集分别建立PLS回归模型。高、中、低筋面粉及混合样品集基于最优区间的PLS模型的决定系数为0.949～0.972,标准误差为0.581%～0.830%,验证集标准偏差与预测标准偏差的比值为4.171～4.830。结果表明,基于最优区间的近红外光谱方法对不同类型面粉中曲酸质量分数为1.0%～10.0%的样品具有较好的预测结果,结合具有低检测限的化学检测方法,在对大批量样品的检测中可提高检测效率。     

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.     

同步荧光光谱法测定橙汁中原果汁的含量

利用同步荧光光谱法对橙汁中原果汁含量进行定量分析。对样本的同步荧光光谱数据进行预处理然后选择不同的光谱波段结合偏最小二乘(partial least squares,PLS)法建立原果汁含量预测模型,并对预测集样本进行预测来验证模型的准确性以及此法的可行性。结果显示:平滑处理结合一阶导数处理后的光谱数据更加适用于模型建立;使用全波段光谱建立的模型性能优于区间波段所建立模型。最终得到的最优模型对预测集进行预测时所得预测均方根误差(Root mean square error of prediction,RMSEP)为0.035832,决定系数(coefficient of determination,R2)为0.972570。由此可以说明同步荧光光谱法结合PLS可实现原果汁含量的定量分析。      

Variations in antioxidant properties of strawberries grown in Brazilian savannah and harvested in different seasons

BACKGROUND: Strawberries are nutritive fruits and a source of antioxidants. We evaluated antioxidant properties of ‘Camino Real’ strawberries grown in the Brazilian savannah, harvested in different seasons. Analytical and meteorological data were analyzed by partial least squares regression. RESULTS: Fruits from May showed the lowest contents of total phenolics (1789.78 mg kg^?1 fresh weight (FW)), catechin (21.37 mg kg^?1 FW), quercetins (4.89 mg kg^?1 FW) and total ellagic acid (208.68 mg kg^?1 FW) and the lowest antioxidant activity by 2,2‐diphenyl‐1‐picrylhydrazyl (DPPH) (11.39 mg Butylated hydroxytoluene (BHT) eq. g^?1 FW) and ferric reducing antioxidant power (FRAP) (22.01 mg ferrous sulfate eq. g^?1 FW) assays. Strawberries harvested in July presented the lowest concentrations of total (190.61 mg kg^?1 FW) and individual anthocyanins (73.88 mg kg^?1 FW and 5.96 mg kg^?1 FW for pelargonidin‐3‐glucoside and cyanidin‐3‐glucoside, respectively), but the highest contents of vitamin C (685.47 mg kg^?1 FW), DPPH (18.87 mg BHT eq. g^?1 FW) and FRAP (39.30 mg ferrous sulfate eq. g^?1 FW). The highest contents of free ellagic acid (26.11 mg kg^?1 FW), pelargonidin‐3‐glucoside (291.82 mg kg^?1 FW) and cyanidin‐3‐glucoside (11.84 mg kg^?1 FW) were found in strawberries from September. Rain in the previous 30 days to harvest influenced negatively many phenolics and antioxidant activity of strawberries harvested in May. In July, longer photoperiod and lower temperature at 30 days previous to harvest probably led to higher antioxidant activity and vitamin C. Increased photoperiod and temperature at the final stage of maturation seem to raise pigments and free ellagic acid in strawberries. CONCLUSION: It was possible to observe significant relationships among meteorological and antioxidant variables for strawberries grown in the Brazilian savannah. Copyright © 2011 Society of Chemical Industry     

Fast determination of boiling time of yardlong bean using visible and near infrared spectroscopy and chemometrics

The performance of visible and near infrared (Vis-NIR) spectroscopy as a rapid and non-destructive technique to determine the boiling time of yardlong beans was investigated. Vis-NIR spectra of beans boiled for 0, 30 to 300 s were measured. Robust least-squares support vector machines (R-LS-SVM) with RBF kernel obtained the best result. Partial least square based variable elimination (VE_PLS) and robust least square-support vector machines based variable elimination (VE_R-LS-SVM) were used for variable selections. Four most important variables at 409, 614, 880, and 984 nm were selected. After the variable selection, 90% of the variables were eliminated and the model’s residual predictive deviation (RPD) only decreased 10% compared to that of the model without the variable elimination. The results showed that the Vis-NIR spectra can be used to determine the boiling time of yardlong beans during the boiling process.     

Analysis of pork adulteration in beef meatball using Fourier transform infrared (FTIR) spectroscopy

Meatball is one of the favorite foods in Indonesia. The adulteration of pork in beef meatball is frequently occurring. This study was aimed to develop a fast and non destructive technique for the detection and quantification of pork in beef meatball using Fourier transform infrared (FTIR) spectroscopy and partial least square (PLS) calibration. The spectral bands associated with pork fat (PF), beef fat (BF), and their mixtures in meatball formulation were scanned, interpreted, and identified by relating them to those spectroscopically representative to pure PF and BF. For quantitative analysis, PLS regression was used to develop a calibration model at the selected fingerprint regions of 1200-1000 cm(-1). The equation obtained for the relationship between actual PF value and FTIR predicted values in PLS calibration model was y = 0.999x + 0.004, with coefficient of determination (R(2)) and root mean square error of calibration are 0.999 and 0.442, respectively. The PLS calibration model was subsequently used for the prediction of independent samples using laboratory made meatball samples containing the mixtures of BF and PF. Using 4 principal components, root mean square error of prediction is 0.742. The results showed that FTIR spectroscopy can be used for the detection and quantification of pork in beef meatball formulation for Halal verification purposes.     

Authentication of Olive Oil Adulterated with Vegetable Oils Using Fourier Transform Infrared Spectroscopy

Determination of the authenticity of extra virgin olive oils has become more important in recent years following some infamous adulteration and contamination scandals. The study focused on application of Fourier transform infrared spectroscopy to identify the adulteration of olive oils. Single-bounce attenuated total reflectance measurements were made on pure olive oil and olive oil samples adulterated with varying concentrations of sunflower oil (20-100 mL vegetable oil/L of olive oil). Discriminant analysis using 12 principal components was able to classify the samples as pure and adulterated olive oils based on their spectra. A partial least squares model was developed and used to verify the concentrations of the adulterant. Furthermore, the discriminant analysis method was used to classify olive oil samples as distinct from other vegetable oils based on their infrared spectra.