Comparison of Various Preparation Methods for Determination of Organic Acids in Fruit Vinegars with a Simple Ion-Exclusion Liquid Chromatography

An ion-exclusion liquid chromatography with mobile phase 0.005 mol L^?1 H₂SO₄ and step flow rate gradient (0.2 mL min^?1 in the first 40 min and 0.5 mL min^?1 from 41 to 60 min) was used to determine 20 organic acids simultaneously at 17 °C within 51 min. The peak resolutions (Rs) were 0.45??3.02 and separation factors (??) were all higher than 1. Impurities in fruit vinegar executed with direct injection or C18 cartridge clean-up for analysis would influence the glutaric and oxalic acid measurement; however, SAX cartridge extraction could reduce the interferences (organic acid recoveries were 93.93??99.98%). Acetic, ascorbic, citric, malic, and malonic acids were the major organic acids in fruit vinegars (apple, apple sparkling, plum, cranberry, and grape).     

Multiproduct,Multicomponent and Multivariate Calibration: a Case Study by Using Vis-NIR Spectroscopy

Vitamin C and total acidity were determined in industrialized fruit nectar and soy juices through visible-near infrared (Vis-NIR) spectroscopy and multiproduct, multicomponent, and multivariate calibration, based on partial least squares (PLS2) regression. Since samples with different types, flavors, and sugar content (light or not) were together in the model construction, the samples present higher heterogeneity and it was necessary to optimize the calibration and validation sets by outlier elimination based on leverage and unmodelled residuals in spectral data. The model was developed and validated by the evaluation of the parameters of merit such as accuracy, analytical sensitivity, adjust, linearity, residual prediction deviation, limits of detection, and quantification. The results achieved indicates that the multiproduct, multicomponent, and multivariate calibration model developed from Vis-NIR spectroscopy and PLS2 regression can be used in the industrial routine analysis as an alternative to titration reference methods that are time- and reagent-consuming methods, making the methodology extremely attractive from the industrial point of view.     

Detection of Aspartic Acid in Fermented Cordyceps Powder Using Near Infrared Spectroscopy Based on Variable Selection Algorithms and Multivariate Calibration Methods

Near infrared (NIR) spectroscopy combined with chemometrics was employed to detect the aspartic acid content in fermented Cordyceps powder. One hundred sixty-nine samples were applied for calibration (n?=?113) and prediction (n?=?56). Six different pretreatment methods were compared to determine the optimal pretreatment method for analysis. The wavelength variables selected by regression coefficient analysis, successive projections algorithm, and genetic algorithm–partial least squares analysis (GAPLS) were used as the inputs of partial least-squares analysis, multiple linear regression (MLR), and least-squares support vector machine. The performances of these calibration methods were also compared to determine the best model. The results indicated that GAPLS-MLR obtained the highest precision with a correlation coefficient of prediction r _pre?=?0.9223, root mean square of prediction RMSEP?=?0.0751 g/100 g, and coefficient of variation CV?=?5.15 %. The overall results showed that NIR was feasible for the determination of aspartic acid in fermented Cordyceps powder and GAPLS could perform well with less input dimension and computation complexity in the aspartic acid estimation.     

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.     

On-Vine Monitoring of Grape Ripening Using Near-Infrared Spectroscopy

This study evaluated the ability of near-infrared (NIR) spectroscopy to characterise the behaviour of white and red grapes during on-vine ripening, as a function of grape position in the bunch (high, middle and low) and bunch orientation (north, south, east and west) and to distinguish between different ripening stages with a view to optimising harvesting times depending on the grape variety and the type of wine to be made. A total of 24 bunches of two wine-grape varieties (cv. Pedro Ximénez and cv. Cabernet Sauvignon) were labelled and analysed directly on the vine using a commercially available handheld micro-electro-mechanical system spectrophotometer (1,600?C2,400?nm). Principal component analysis was performed to study relationships between the various configurations (grape position and bunch orientation), ripening stages and spectral data. Results for the white-grape variety showed that grapes high on the bunch behaved differently during ripening from those in central or low positions and that east-facing bunches behaved differently from the rest. For both varieties, analysis of bunch spectral characteristics enabled three stages of ripening to be distinguished: early, middle and late. Subsequently, the ability of NIR technology to classify wine grapes as a function of reducing-sugar content, with a view to optimising harvest timing, was evaluated by partial least squares discriminant analysis: 88?% of white grapes and 88?% of red grapes were correctly classified while over 79?% of samples were correctly assigned to representative groups. These results confirmed that NIR technology in the spectral range 1,600?C2,400?nm is an appropriate technique for on-vine monitoring of the ripening process, enabling selective harvesting depending on the type of wine to be made.     

Differentiation of Frozen and Unfrozen Beef Using Near-Infrared Spectroscopy

Freezing and thawing affects the quality of meat. The present paper focuses on using near-infrared (NIR) diffuse reflectance spectroscopy to detect whether beef has been frozen and thawed. Intact beef and drip or centrifuged meat juice of M longissimus dorsi slices from 40 cattle were used as samples. The meat juices were analysed using dry extract spectroscopy by infrared reflection (DESIR). From centrifuged juice 80 samples were classified 100% correctly, using crossvalidation, into frozen or unfrozen beef by the K nearest neighbours method. This was obtained by high-order principal components from 400–2500 nm spectra. Other multivariate techniques, smaller wavelength ranges and detecting refrozen, thawed beef also gave results between 90 and 100%. Analyses of drip loss, exudative properties, water-holding capacity and dry matter of meat juice supported the interpretation of the NIR measurements. The results showed that NIR might be used as a screening method to differentiate unfrozen and frozen beef. © 1997 SCI.     

Determination of Amino Acids in Wheat and Barley by Near-Infrared Reflectance Spectroscopy

The use of NIR spectroscopy for the prediction of amino acid compositions in wheat and barley has been demonstrated. Using 2nd derivative mathematical treatments of log I/R data of ground wheat samples obtained on the Beltsville Universal Computerized Spectrophotometer, the concentration of lysine, threonine, tryptophan and methionine could be predicted with accuracies acceptable for screening purposes. With barley samples low standard errors of prediction (high accuracy) were obtained for I3 amino acids using a research NIR and a commercial bench-type instrument. For both of these instruments, use of 1st derivative mathematical treatments of spectral data gave the best accuracy. Results indicated that NIR techniques may be very useful in plant breeding programs and in the food and feed industry for the rapid and accurate analysis of amino acids.     

Real-Time Monitoring of Organic Carrot (var. Romance) During Hot-Air Drying Using Near-Infrared Spectroscopy

The worldwide consumption of dried carrot (Daucus carota L.) is on a growing trend. Conventional methods for drying carrots include hot-water blanching followed by hot-air drying, which is usually uncontrolled and therefore prone to product quality deterioration. Thus, there is a need for innovative drying systems that yield high-value end products. In this study, the efficacy of NIR spectroscopy for the non-destructive monitoring of physicochemical changes and drying behaviour in organic carrot slices during 8-h hot-air drying at 40 °C was demonstrated using Partial least squares (PLS) regression and PLS discriminant analysis (PLS-DA). The impact of hot-water blanching pre-treatment (at 95 °C for 1.45 min) for enzyme inactivation on performances of both regression and classification models was also evaluated. PLS regression models were successfully developed to monitor changes in water activity (R ² = 0.91–0.96), moisture content (R ² = 0.97–0.98), total carotenoids content (R ² = 0.92–0.96), lightness for unblanched carrots (R ² = 0.80–0.83) and hue angle for blanched samples (R ² = 0.85–0.87). Soluble solids content prediction was poor for both treatments (RMSEP = 3.43–4.40). Classification models were developed to recognise dehydration phases of carrot slices on the basis of their NIR spectral profile using K-means and PLS-DA algorithms in sequence. The performance of each PLS-DA model was defined based on its accuracy, sensitivity and specificity rates. All of the selected models provided from good (> 0.85) to excellent (> 0.95) sensitivity and specificity for the predefined drying phases. Feature selection procedures yielded both regression and classification models with performances very similar to models computed from the full spectrum.     

基于近红外光谱的腌制期咸鸭蛋理化指标无损检测

以高邮麻鸭蛋为研究对象,采集整个腌制期咸鸭蛋近红外光谱数据,对比测定其理化指标值,建立咸鸭蛋关键品质指标（蛋黄含水率、蛋黄氯化钠浓度和咸蛋黄指数）的无损快速检测模型。为尽可能削弱外部其他因素对样本光谱采集过程的影响,使用多元散射校正、归一化等预处理方法,结合竞争性自适应重加权算法（competitive adaptive reweighted sampling,CARS）、连续投影算法（successive projections algorithm,SPA）和非信息变量剔除（uninformative variables elimination,UVE）3种特征选择算法建立偏最小二乘回归模型,并在一次特征波段选取基础上提取二次特征波长,再建立偏最小二乘回归模型。结果表明,蛋黄含水率、蛋黄氯化钠浓度和咸蛋黄指数的最优波长选择方法均是二次特征波段提取法UVE+SPA,发现二次特征波段选择综合表现最优。经对比分析,蛋黄含水率、蛋黄氯化钠浓度、咸蛋黄指数最优模型结构分别是标准化-UVE+SPA-PLSR、卷积平滑-UVE+SPA-PLSR、均值中心化-UVE+SPA-PLSR,训练集相关系数...     

Non-Volatile Organic Acids,pH and Titratable Acidity Changes in Pineapple Fruit Slices During Frozen Storage

Effects of freezing and frozen storage on pH, titratable acidity and non-volatile organic acids of two pineapple fruit cultivars, Smooth Cayenne and Red Spanish, were studied. Pineapple fruit was frozen as slices in a cold room at −18°C and stored at this temperature for a 12 month period. A negative correlation was found between pH and titratable acidity in the two cultivars throughout frozen storage ( r =-0·67 for Smooth Cayenne and r =-0·71 for Red Spanish). Non-volatile organic acids were determined by high performance liquid chromatography. The major components were citric and L-malic acids. A high correlation was found between these two acids during frozen storage ( r =0·75 in Smooth Cayenne and r =0·78 in Red Spanish). There were significant differences ( P⩽ 0·01) in pH and titratable acidity between the two studied varieties after a year of frozen storage. Significant differences ( P⩽ 0·05) were found in pH values during frozen storage in cv Smooth Cayenne and in citric and L-malic acids in cv Red Spanish. Freezing preservation of pineapple fruit slices led to minimal chemical changes after a year of frozen storage.     

Characterization of Volatile Organic Compounds of Vinegars with Novel Electronic Nose System Combined with Multivariate Analysis

A novel electronic nose system (also called artificial olfaction system) based on colorimetric sensor array was developed for characterization and identification of the volatile organic compounds (VOCs) of vinegars fermented from different raw materials. Fifteen chemo-responsive dyes including nine metalloporphyrins and six pH indicators were selected according to their sensitivity to volatile compounds from vinegar samples. The colorimetric sensor array was made by printing selected chemo-responsive dyes on a silica gel plate. A color change profile for each sample was obtained by differentiating the images of the colorimetric sensor array before and after exposure to the odorant of vinegar sample. The digital data (i.e., red, green, and blue components of the image) representing the color change profiles for the vinegar samples were analyzed. Genetic algorithm partial least squares was employed to select sensitive image digital variable to build a calibration model. Several methods (i.e., linear discrimination analysis, LDA; partial least square discrimination analysis; artificial neural network) were also used comparatively for classification, the result was evaluated by the % correct identification of samples. The optimal model was achieved by LDA model with 14 image digital variables used, and all the vinegar samples were correctly identified both in training and testing sets. This research suggests that the system shows significant potential in vinegar VOCs characterization and discrimination.     

Predictive Analysis of Cocoa Procyanidins Using Near-Infrared Spectroscopy Techniques

ABSTRACT A near-infrared reflectance (NIR) spectroscopy technique for the prediction of procyanidins in cocoa beans ( Theobroma cacao ) has been developed. A select group of cocoa liquors from different origins used for the manufacture of chocolate were analyzed to determine quantitative levels of procyanidin oligomers (monomer to decamer) using normal-phase high-performance liquid chromatography. The calibration set included seasonal, environmental, and fermentation variations to cover the concentration range of procyanidins found at naturally occurring levels. The sample set was analyzed on FOSS NIR System II 6500 spectrophotometers and partial least-squares algorithms developed. Very good calibration statistics were obtained for the prediction of the total procyanidin oligomers ( r 2 = 0.983) with standard deviation/standard error of cross validation ratio (SD/SECV) of 5.68. A global version of this calibration, involving 20 Foss instruments produced a calibration r 2 = 0.98 with a SD/ SECV = 6.20. Procyanidins have attracted increasing attention because of the rapidly growing body of evidence associating these compounds with a wide range of potential health benefits. A rapid method for the analysis of procyanidins in cocoa liquors would be beneficial in quality-control environments and would provide cost benefits to manufacturing operations.     

Authenticity Control of Roasted Coffee Brands Using Near-Infrared Spectroscopy

In this work, a methodology based on near-infrared spectroscopy (NIRS) was exploited in order to discriminate between commercial coffee brands. The main advantages of this approach compared to other strategies (e.g., wet chemistry methods) are its lower cost, less labor, and lower time per analysis. Two commercial brands were discriminated among several others present in the Portuguese market. The chemometric method used to estimate discriminant models was partial least squares discriminant analysis (PLSDA). Results show that it is possible to discriminate coffee brands using this strategy with a correct classification of 100 %. The spectral region, more favorable to discrimination of roasted coffee brands, can be related with differences in the concentrations of compounds, such as, chlorogenic acid and sucrose, and also due to differences on lipid fraction. This methodology is adequate for field implementation, namely, adopting handheld NIRS instruments.     

提取近红外光谱有效变量快速检测猪肉挥发性盐基氮

以市售新鲜冷藏(4℃)猪肉为研究对象,采用蒙特卡洛-无信息变量消除算法和连续投影算法对原始近红外光谱的800个波长变量进行提取,共筛选出与挥发性盐基氮含量直接和间接相关的有效波长变量36个,并采用偏最小二乘法构建预测模型,验证集的相关系数和标准偏差分别为0.876 4和1.205 7 mg/100 g。     

Sensory Quality Evaluation of Rice Using Visible and Shortwave Near-Infrared Spectroscopy

This research aimed to develop both visible and shortwave near-infrared spectroscopy to evaluate the sensory qualities of cooked rice. In this study, four different types of milled rice were used: parboiled, white, new Jasmine, and aged Jasmine. The sensory qualities of cooked rice (adhesiveness, hardness, stickiness, dryness, whiteness, and aroma) were evaluated by a trained sensory panel. The results demonstrated that these sensory attributes correlated with visible and shortwave near-infrared spectral data. Both visible and shortwave near-infrared spectroscopy models used for predicting the sensory qualities of cooked rice were established using partial least squares regression. All prediction results for sensory qualities showed a range of R²_val between 0.837 and 0.918, with the highest found for aroma (0.918). The proposed models can be utilized in quality control by the rice industry.     

Nondestructive Differentiation of Panax Species Using Visible and Shortwave Near-Infrared Spectroscopy

Visible and short-wave near-infrared (Vis-SWNIR) spectroscopy was investigated to differentiate species of panax, including American Panax quinquefoliumI, Chinese Panax quinquefolium, and Chinese Panax ginseng. Principal component analysis (PCA) was applied before least-square support vector machine (LS-SVM) modeling, and the vast points of the spectral data (376–1,025 nm) were effectively reduced. PCA-LS-SVM differentiated species with 100% correct classification rate for the tested samples. In addition, effective wavelengths were selected according to modeling power, discrimination power, regression coefficients, loading weights, and genetic algorithms, respectively. The optimal and simplified LS-SVM model with 100% correct classification rate was achieved using the effective wavelengths selected by genetic algorithms. The results showed that Vis-SWNIR spectroscopy technique can be applied as a high accuracy and fast way for the qualitative discrimination of herb species.     

Investigations of Native and Resistant Starches and Their Mixtures Using Near-Infrared Spectroscopy

Resistant starches (RS) play important roles in our nutrition; therefore, the investigation of these starches is notably important. In our study, two native starches (maize and wheat) and two resistant starches (Hi-maize™260, high amylose maize starch as RS2 and Fibersym™70, phosphorylated wheat starch as RS4) were investigated as is and in their physical mixtures (samples containing 20%, 40%, 60% and 80% RS) using near-infrared (NIR) spectroscopy. The aim of our study was to examine the spectra of resistant starches and to differentiate the resistant starch components in different ratios by NIR spectroscopy. The differences of samples were presented in two characteristic absorption bands for carbohydrate: carbohydrate II (2,080–2,130 nm) and carbohydrate III (2,275–2,290 nm) regions. Additionally, principal component analysis (PCA) for all samples was carried out. It was shown that the increasing amount of amylose can be sensitively followed up in carbohydrate II region. The phosphorylated RS4 is not so characteristic probably due to the reduced mobility of amorphous chains; however, the RS4 addition can be observed. Additionally, it was proven that the carbohydrate III region is sensitive for the changes of amylose–amylopectin ratio as well. The wheat-based RS4 addition causes linear changes in maize-based mixtures; thus the botanical origin is determining in this region. The global PCA analysis justified that the RS2 addition can be sensitively followed up independent on the medium; however, the increasing amount of RS4 cannot be detected in the PCA plot. The loading spectra of PC1 component attribute great significance to the carbohydrate III region.