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.     

四川不同地区的熟醋及其固态发酵过程中有机酸含量变化分析

为了解不同地区的四川麸醋中有机酸含量差异及其固态发酵过程中有机酸含量变化,采用高效液相色谱对其7种主要的有机酸含量进行测定。结果表明,在四川麸醋固态发酵过程中,乙酸由1.2712 mg/mL升高至3.1214 mg/mL,是含量最高的酸,占总有机酸58.12%。而含量次之的是乳酸,由0.8858 mg/mL升高至1.3216 mg/mL,占总有机酸32.67%,其中乙酸和乳酸变化最为显著,草酸、柠檬酸、酒石酸、柠檬酸和酒石酸变化不明显。对结果进行主成分分析发现,在熟醋中,乳酸对四川麸醋显示了较高的相关性,四川麸醋中乳酸的含量明显高于其它地区熟醋中乳酸的含量,这表明乳酸可以将四川麸醋与其它地域的食醋有效的区分开。在四川麸醋固态发酵过程中,发酵前期3d^10d与乳酸体现了较高的关联性,后期12 d^23 d与乙酸具有较高的相关性。     

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.     

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.     

Simultaneous measurement of total acid content and soluble salt-free solids content in Chinese vinegar using near-infrared spectroscopy

Total acid content (TAC) and soluble salt-free solids content (SSFSC) in Chinese vinegar are 2 important indicators in the assessment of its quality. This paper shows the feasibility to determine TAC and SSFSC in Chinese vinegar by near-infrared (NIR) spectroscopy. Synergy interval partial least square (Si-PLS) algorithm was performed to calibrate the regression model. The number of PLS factors and the number of intervals were optimized simultaneously by cross-validation. The performance of the model was evaluated according to root mean square error of prediction (RMSEP) and correlation coefficient (R) in the prediction set. The optimum Si-PLS model for TAC was achieved with RMSEP = 0.264 g/100 mL and R(p) = 0.9655; the optimum Si-PLS model for SSFSC was achieved with RMSEP = 1.93 g/100 mL and R(p) = 0.9302. The overall results demonstrated that NIR spectroscopy combined with Si-PLS could be utilized to determinate TAC and SSFSC in Chinese vinegar, and NIR spectroscopy has a potential to be used in vinegar industry.     

原汁苹果醋中的有机酸

以苹果汁为原料 ,采用液态酒精发酵和醋酸发酵法制作苹果醋 ,采用反相高效液相色谱法分析苹果醋中有机酸 ,研究了苹果品种、醋酸菌种、醋酸发酵方法及过程对有机酸的影响。认为苹果酸是苹果醋中的主要有机果酸 ,其与液态粮食醋有机酸组成的差别主要体现在苹果酸、酒石酸和乳酸的含量上 ,苹果品种是影响原汁苹果醋中有机酸的种类和含量主要因素 ,醋酸菌在醋酸发酵过程中有代谢各种有机酸的作用 ,发酵方法会影响醋酸菌对有机酸代谢作用的程度 ,试验中所采用的 5种醋酸菌在醋酸发酵过程中通过消耗苹果酸、乳酸、琥珀酸 ,而产生和积累柠檬酸。     

Non-destructive analysis of anthocyanins in cherries by means of Lambert-Beer and multivariate regression based on spectroscopy and scatter correction using time-resolved analysis

In high-value sweet cherry (Prunus avium), the red coloration - determined by the anthocyanins content - is correlated with the fruit ripeness stage and market value. Non-destructive spectroscopy has been introduced in practice and may be utilized as a tool to assess the fruit pigments in the supply chain processes. From the fruit spectrum in the visible (Vis) wavelength range, the pigment contents are analyzed separately at their specific absorbance wavelengths.A drawback of the method is the need for re-calibration due to varying optical properties of the fruit tissue. In order to correct for the scattering differences, most often the spectral intensity in the visible spectrum is normalized by wavelengths in the near infrared (NIR) range, or pre-processing methods are applied in multivariate calibrations.In the present study, the influence of the fruit scattering properties on the Vis/NIR fruit spectrum were corrected by the effective pathlength in the fruit tissue obtained from time-resolved readings of the distribution of time-of-flight (DTOF). Pigment analysis was carried out according to Lambert-Beer law, considering fruit spectral intensities, effective pathlength, and refractive index. Results were compared to commonly applied linear color and multivariate partial least squares (PLS) regression analysis. The approaches were validated on fruits at different ripeness stages, providing variation in the scattering coefficient and refractive index exceeding the calibration sample set.In the validation, the measuring uncertainty of non-destructively analyzing fruits with Vis/NIR spectra by means of PLS or Lambert-Beer in comparison with combined application of Vis/NIR spectroscopy and DTOF measurements showed a dramatic bias reduction as well as enhanced coefficients of determination when using both, the spectral intensities and apparent information on the scattering influence by means of DTOF readings. Corrections for the refractive index did not render improved results.     

反相HPLC快速测定调味品中有机酸

以反相高效液相色谱测定了酱油、食醋中的主要有机酸，在ODS2柱上，以5％CH3OH－0.10mol.L^-1KH2PO4(pH3.0)溶液作流动相，流速1.0mL/min，UV215nm进行检测。方法简便、快速，样品加标回收率在95％－110％范围内。     

Quantification of Nitrogen Status in Rice by Least Squares Support Vector Machines and Reflectance Spectroscopy

The estimation of nitrogen status non-destructively in rice was performed using canopy spectral reflectance with visible and near-infrared reflectance (Vis/NIR) spectroscopy. The canopy spectral reflectance of rice grown with different levels of nitrogen inputs was determined at several important growth stages. This study was conducted at the experiment farm of Zhejiang University, Hangzhou, China. The soil plant analysis development (SPAD) value was used as a reference data that indirectly reflects nitrogen status in rice. A total of 64 rice samples were used for Vis/NIR spectroscopy at 325–1075 nm using a field spectroradiometer, and chemometrics of partial least square (PLS) was used for regression. The correlation coefficient (r), root mean square error of prediction, and bias in prediction set by PLS were, respectively, 0.8545, 0.7628, and 0.0521 for SPAD value prediction in tillering stage, 0.9082, 0.4452, and −0.0109 in booting stage, and 0.8632, 0.7469, and 0.0324 in heading stage. Least squares support vector machine (LS-SVM) model was compared with PLS and back propagation neural network methods. The results showed that LS-SVM was superior to the conventional linear and non-linear methods in predicting SPAD values of rice. Independent component analysis was executed to select several sensitive wavelengths (SWs) based on loading weights; the optimal LS-SVM model was achieved with SWs of 560, 575–580, 700, 730, and 740 nm for SPAD value prediction in booting stage. It is concluded that Vis/NIR spectroscopy combined with LS-SVM regression method is a promising technique to monitor nitrogen status in rice.     

Differentiation of apple juice samples on the basis of heat treatment and variety using chemometric analysis of MIR and NIR data

The potential of mid-infrared (MIR) and near-infrared (NIR) spectroscopy for their ability to differentiate between apple juice samples on the basis of apple variety and applied heat-treatment was evaluated. The heat-treatment involved exposure of juice samples (15 ml) for 30 s in a 900 W microwave oven and the apple varieties used to produce the juice samples were Bramley, Elstar, Golden Delicious and Jonagold. The chemometric procedures applied to the MIR and NIR data were partial least squares regression (PLS1 for differentiation on the basis of heat-treatment, PLS2 for varietal differentiation) and linear discriminant analysis (LDA) applied to principal component (PC) scores. PLS1 and PLS2 gave the highest level of correct classification of the apple juice samples according to heat-treatment (77.2% for both MIR and NIR data) and variety (78.3–100% for MIR data; 82.4–100% for NIR data), respectively.     

Visible/Near-Infrared Spectra for Linear and Nonlinear Calibrations: A Case to Predict Soluble Solids Contents and pH Value in Peach

Two sensitive wavelength (SWs) selection methods combined with visible/near-infrared (Vis/NIR) spectroscopy were investigated to determine the soluble solids content (SSC) and pH value in peaches, including latent variables analysis (LVA) and independent component analysis (ICA). A total of 100 samples were prepared for the calibration (n = 70) and prediction (n = 30) sets. Calibration models using SWs selected by LVA and ICA were developed, including linear regression of partial least squares (PLS) analysis and nonlinear regression of least squares-support vector machine (LS-SVM). In the nonlinear models, four SWs selected by ICA achieved the optimal ICA-LS-SVM model compared with LV-LS-SVM and both of them better than linear model of PLS. The correlation coefficients (r _p and r _cv), root mean square error of cross validation, root mean square error of prediction, and bias by ICA-LS-SVM were 0.9537, 0.9485, 0.4231, 0.4155, and 0.0167 for SSC and 0.9638, 0.9657, 0.0472, 0.0497, and −0.0082 for pH value, respectively. The overall results indicated that ICA was a powerful way for the selection of SWs, and Vis/NIR spectroscopy incorporated to ICA-LS-SVM was successful for the accurate determination of SSC and pH value in peach.     

Rapid detecting total acid content and classifying different types of vinegar based on near infrared spectroscopy and least-squares support vector machine

More than 3.2 million litres of vinegar is consumed every day in China. There are many types of vinegar in China. How to control the quality of vinegar is problem. Near infrared spectroscopy (NIR) transmission technique was applied to achieve this purpose. Ninety-five vinegar samples from 14 origins covering 11 provinces in China were collected. They were classified into mature vinegar, aromatic vinegar, rice vinegar, fruit vinegar, and white vinegar. Fruit vinegar and white vinegar were separated from the other traditional categories in the two-dimension principal component space of NIR after principle component analysis (PCA). Least-squares support vector machine (LS-SVM) as the pattern recognition was firstly applied to identify mature vinegar, aromatic vinegar, rice vinegar in this study. The top two principal components (PCs) were extracted as the input of LS-SVM classifiers by principal component analysis (PCA). The best experimental results were obtained using the radial basis function (RBF) LS-SVM classifier with σ = 0.8. The accuracies of identification were more than 85% for three traditional vinegar categories. Compared with the back propagation artificial neural network (BP-ANN) approach, LS-SVM algorithm showed its excellent generalisation for identification results. As total acid content (TAC) is highly connecting with the quality of vinegar, NIR was used to prediction the TAC of samples. LS-SVM was applied to building the TAC prediction model based on spectral transmission rate. Compared with partial least-square (PLS) model, LS-SVM model gave better precision and accuracy in predicting TAC. The determination coefficient for prediction (R_p) of the LS-SVM model was 0.919 and root mean square error for prediction (RMSEP) was 0.3226. This work demonstrated that near infrared spectroscopy technique coupled with LS-SVM could be used as a quality control method for vinegar.     

浙江玫瑰醋发酵过程中细菌菌群结构变化与有机酸形成相关性分析

基于Illumina MiSeq高通量测序对浙江玫瑰醋“冲缸放水”后的醋样中细菌V4区进行测序,并用高效液相色谱对醋样有机酸含量进行测定,得出玫瑰醋发酵过程中细菌相对丰度以及有机酸含量的变化,并用双向正交偏最小二乘（bidirectional orthogonal partial least squares,O2PLS）法模型对两者相关性进行分析,结果表明：玫瑰醋在“冲缸放水”后的发酵过程中,醋酸杆菌属和乳酸杆菌属在细菌菌群中占绝对优势（相对丰度之和大于80%）;玫瑰醋中有机酸含量一直呈现上升趋势,乙酸和乳酸含量最高,对玫瑰醋有机酸含量进行系统聚类分析和主成分分析分析,可以将醋样分为发酵前期、发酵中期、发酵后期三大类,各类醋样有机酸组成差异显著;利用O2PLS模型对细菌与有机酸相关性分析,得到23 个与有机酸相关重要性指标（VIP（pred））大于1的细菌属,包括Acetobacter、Lactobacillus、Methylobacterium等;对VIP（pred）大于1的细菌与有机酸进行相关性分析,作出细菌与有机酸相关性系数Heatmap,并得出与各种有机酸相关性系数|ρ|＞0.6的高度相关细菌属。为找寻玫瑰醋发酵过程中的功能微生物,提升玫瑰醋品质提供数据支持。     

木瓜酒和木瓜醋发酵工艺及其有机酸组成分析

以光皮木瓜为原料,发酵木瓜干酒,再通过液态深层发酵酿制木瓜醋,确定了木瓜干酒和木瓜醋的发酵工艺,并对木瓜酒和醋主要有机酸进行分析。光皮木瓜经榨汁、调整糖度后进入发酵工序,酒精发酵采用带皮渣半固态发酵方式,条件为加水比例1.5∶1（m/m）、初始糖度18%、果酒干酵母接种量0.1%、在24 ℃条件下发酵64 h,木瓜酒酒度（乙醇体积分数）为9.45%。醋酸发酵采用半连续式液态深层发酵法,调整初始酒度7%,醋酸菌接种量10%,在34 ℃条件下醋酸发酵80 h,木瓜醋总酸度为4.52%;分割留种发酵仅需24 h即可完成醋酸发酵。采用反相高效液相色谱法从木瓜干酒和木瓜醋中检出10 种有机酸,分别是草酸、酒石酸、甲酸、苹果酸、α-酮戊二酸、乳酸、醋酸、柠檬酸、富马酸和琥珀酸。实验确定的发酵工艺以及有机酸的分析与鉴定可为木瓜干酒及木瓜醋产品的开发提供理论依据。     

遗传算法联合LS-SVM的苹果原醋成分定量分析

利用近红外光谱技术对苹果原醋中的重要指标进行定量分析,并进行模型优化以提高性能。采用遗传偏最小二乘法（GA-PLS）提取的特征波长作为最小二乘支持向量机（LS-SVM）的输入变量,先后建立苹果原醋中总酸、可溶性固形物的近红外定量模型,并与建立的偏最小二乘（PLS）模型结果进行比较。用决定系数（R2）、预测均方根误差（RMSEP）以及相对分析误差（RPD）对模型进行评价,确定最佳建模方法。结果表明,相比于PLS模型,总酸及可溶性固形物指标的LS-SVM定量模型的R2、RMSEP以及RPD值均有更好的表现,且在进行独立测试集验证时,LS-SVM模型的预测精度也明显优于PLS模型。说明遗传算法联合LS-SVM建立的定量模型有很高的准确度及稳定性,可以应用于苹果原醋总酸和可溶性固形物含量的快速检测。     

枇杷蜂蜜中饴糖的近红外光谱检测

实验通过对纯枇杷蜂蜜及主动掺入1%、2%……30%饴糖的假枇杷蜂蜜进行近红外光谱扫描,采用TQAnalysisv6对数据进行预处理,建立饴糖含量的定性及偏最小二乘法和主成分回归法定量分析模型,并将模型应用于蜂蜜样品的分析预测。结果显示,采用原始光谱或一阶微分处理建立的判别分析模型均能够较好地区分掺饴糖蜂蜜与纯蜂蜜。根据PLS算法、PCR算法建立的定量模型相关系数分别为0.99771、0.98654,用于预测的蜂蜜样品实际值与预测值之间的决定系数分别为0.992、0.974。由此可见,用近红外光谱技术鉴别蜂蜜中是否添加饴糖是可行的,在实际操作中可以采用近红外光谱法快速定性判别蜂蜜中是否含有饴糖,也可根据化学计量法确定饴糖的含量,为蜂蜜打假提供依据。     

近红外结合线性回归算法快速预测小麦籽粒干物质和重量

为了实现小麦品质(干物质、重量)的快速无损检测,对35个小麦品种样品进行了近红外系统扫描,获取光谱信息,并进行高斯滤波平滑(GFS)、归一化(N)和基线校正(BC)预处理.采用偏最小二乘(PLS)算法分别建立光谱信息与干物质和重量参考值之间的定量关系.采用回归系数法(RC)和连续投影算法(SPA)两种方法在900～17...     

山西老陈醋醋酸发酵过程中有机酸的变化分析

以山西老陈醋醋醅为实验样品,对其水分含量、pH值、总酸度进行了分析,并通过高效液相色谱技术(HPLC)对其中9种有机酸的组成及动态变化进行了分析.结果表明,醋醅中水分含量较高,始终维持在61％～64％之间;总酸度上升趋势显著,在6.37％～12.17％之间波动;pH值则一直维持在3.5～3.8之间.HPLC分析结果表明,乙酸、乳酸、琥珀酸、苹果酸以及总有机酸的含量增长趋势较明显,草酸、酒石酸、丙酮酸与富马酸在发酵过程中含量不高,且变化趋势不明显.     

Rapid measurement of total acid content (TAC) in vinegar using near infrared spectroscopy based on efficient variables selection algorithm and nonlinear regression tools

Total acid content (TAC) is an important index in assessing vinegar quality. This work attempted to determine TAC in vinegar using near infrared spectroscopy. We systematically studied variable selection and nonlinear regression in calibrating regression models. First, the efficient spectra intervals were selected by synergy interval PLS (Si-PLS); then, two nonlinear regression tools, which were extreme learning machine (ELM) and back propagation artificial neural network (BP-ANN), were attempted. Experiments showed that the model based on ELM and Si-PLS (Si-ELM) was superior to others, and the optimum results were achieved as follows: the root mean square error of prediction (RMSEP) was 0.2486 g/100mL, and the correlation coefficient (R(p)) was 0.9712 in the prediction set. This work demonstrated that the TAC in vinegar could be rapidly measured by NIR spectroscopy and Si-ELM algorithm showed its superiority in model calibration.     

四川麸醋发酵过程中醋醅理化指标及有机酸变化分析

以四川麸醋醋醅为研究对象,利用国标方法和高效液相色谱（HPLC）技术分别对不同发酵时期醋醅的理化指标及有机酸含量的变化进行分析。结果表明,不同发酵时期醋醅的理化指标具有较大的差异。醋醅中水分含量呈先升后降的趋势,在发酵3 d时最高（63.90%）;pH值缓慢降低,但始终维持在4.0～4.4之间,总酸含量呈增长趋势,两者之间呈负相关;挥发酸、不挥发酸与总酸变化趋势大致相同;氨基酸态氮含量呈先快后慢的上升趋势;还原糖含量先升高后降低,发酵9 d时含量最高（10.13 g/100 g干醅）,淀粉含量则呈下降趋势。从醋醅样品中共检测出8种有机酸,分别为草酸、酒石酸、丙酮酸、苹果酸、乳酸、乙酸、柠檬酸、琥珀酸,随着发酵的进行均呈增长趋势,其中乙酸和乳酸为主体有机酸。