基于可见-近红外光谱的生鲜紫薯食味品质快速无损检测

目的建立可见-近红外光谱法快速无损检测生鲜紫薯的熟化黏度和甜度,并对其食味品质进行评价。方法以生鲜紫薯为研究对象,用竞争性自适应加权算法(competitive adaptibe reweighted sampling,CARS)筛选出生鲜紫薯预处理后的光谱数据的特征波长,再用偏最小二乘法(partial least square,regression,PLS)建立了生鲜紫薯熟化峰值扭矩和可溶性糖含量的定量预测模型。另外,选取12个生鲜紫薯根据划分分值区间法对紫薯食味品质进行评价。结果生鲜紫薯峰值扭矩模型预测集的相关系数r为0.9195,均方根误差(root mean square error of prediction,RMSEP)为0.0526 Nm;可溶性糖含量模型的预测集的相关系数r为0.9515,RMSEP为0.3100 mg/g。结论基于可见-近红外光谱技术可以对生鲜紫薯食味品质进行初步快速无损评价,为鲜食性紫薯品质评价提供理论参考。     

可见/近红外漫反射光谱无损检测甜柿果实硬度

该研究的目的是建立可见/近红外漫反射光谱无损检测甜柿果实硬度的数学模型,评价可见/近红外漫反射光谱无损检测甜柿果实硬度的应用价值。果实硬度采用果皮脆性、果皮强度和果肉平均硬度作为评价指标。在可见/近红外光谱区域(400～2 500 nm),采用改进偏最小二乘法,对比分析了不同导数处理、不同散射及标准化处理的甜柿果实硬度定标模型。结果表明,对于果皮强度和果皮脆性,采用最小偏二乘法、一阶导数处理和标准多元离散校正处理建立的定标模型预测效果较好,RP2分别为0.858和0.862,SEP分别为0.094和0.157,RPD分别为2.47和2.63。对于果肉平均硬度,采用改进偏最小二乘法、一阶导数处理和标准正常化和去散射处理建立的定标模型预测效果较好,RP2为0.82,SEP为0.063,RPD为2.35。因此,可见/近红外漫反射光谱无损检测技术可用于甜柿果实硬度的无损检测。     

梨果实近红外光谱无损检测技术研究进展

梨果实容易发生果肉褐变,即使外观完好,也有可能内部发生劣变。近红外光谱(NIRS)分析技术以其快速、无损、多组分同时测定等优势,近年来在梨果实无损检测应用的研究中取得重要进展。本文介绍了NIRS检测技术的原理与方法,综述了国内外应用NIRS无损检测技术在梨糖度、酸度、硬度等内部品质、褐变和损伤等内部缺陷方面及便携式检测的研究进展,并对其存在的问题及应用前景进行了展望。     

生鲜紫薯花青素等多品质参数的可见-近红外快速无损检测

基于实验室自行搭建的可见-近红外光谱系统,以市售生鲜紫薯为研究对象,探讨其花青素、可溶性固形物（soluble solid contents,SSC）以及总糖（total sugars,TS）的同时快速无损检测方法。对紫薯原始光谱进行SG（Savitzky-Golay）平滑、标准正态变量变换以及一阶求导预处理,然后用偏最小二乘回归法进行建模分析。对于花青素和TS,经SG平滑结合一阶求导预处理的模型预测效果最佳;对于SSC,经SNV预处理的模型预测效果最好。针对紫薯各参数最佳预处理光谱采用竞争性自适应加权算法进行波长筛选,再次建立模型。花青素模型预测集的相关系数为0.942 1,预测均方根误差（root mean square error of prediction,RMSEP）为0.225?9?mg/g;SSC模型预测集相关系数为0.943?1,RMSEP为0.878?7?°Brix;TS模型预测集的相关系数为0.925?3,RMSEP为0.244?3%。结果显示,利用可见-近红外光谱可以实现对生鲜紫薯的花青素、SSC以及TS的同时快速无损检测,对生鲜紫薯品质的快速无损检测分选有着重要的实用意义。     

可见-近红外高光谱成像技术对灵武长枣VC含量的无损检测方法

为探究基于高光谱成像技术预测灵武长枣VC含量的可行性并寻找最佳预测模型。采集100?个长枣样本在波长400～1?000?nm处的高光谱图像,对光谱数据进行预处理;应用遗传算法（genetic algorithm,GA）、连续投影算法（successive projection algorithm,SPA）和竞争性正自适应加权（competitive adaptive reweighted sampling,CARS）算法对原始光谱数据提取特征波长;分别建立基于全光谱和特征波长的偏最小二乘（partial least squares regression,PLS）和最小二乘支持向量机（least squares support vector machine,LSSVM）VC含量预测模型。结果表明,采用标准正态变换预处理算法效果最优,其PLS模型的交叉验证相关系数为0.839?5,交叉验证均方根误差为16.248?2;利用GA、SPA和CARS从全光谱的125?个波长中分别选取出12、5?个和26?个特征波长;基于CARS建立的PLS模型效果最优,其Rc、Rp、校正均方根误差、预测均方根误差分别为0.896?2、0.889?2、10.746?2%、12.145?3%。研究结果表明基于高光谱成像技术对灵武长枣VC含量的无损检测是可行的。     

基于近红外技术无损检测深州蜜桃果实内部品质

为了寻求一种快速、无损检测“深州蜜桃”果实内部品质的方法,该研究中采用SACMI近红外分析仪以漫反射方式对采收期果实可溶性固形物含量（Soluble Solid Content,SSC）、硬度、pH进行了无损检测,并与传统参考方法所测结果进行比较。结果表明,剔除异常值后,SSC无损检测预测值与传统参考方法实测值的两组数据相关性（R2=0.79,p<0.01,SEP=0.47）优于硬度（R2=0.47,p<0.01,SEP=2.01）及pH（R2=0.40,p<0.01,SEP=0.14）;SSC模型（RPD=2.15,RMSEP=0.79%,RSD=6.2%）预测性能较高,可满足于快速检测,pH模型（RPD=1.29,RMSEP=0.16,RSD=3.1%）、硬度模型（RPD=1.37,RMSEP=2.37 kg/cm2,RSD=39.4%）预测性能较低。研究表明：采用近红外分析仪检测“深州蜜桃”果实SSC是可行的,可为果实内部品质的评价提供了实时、快速、无损的检测方法,进而为其智能分级提供理论基础与技术支持。     

基于CWT和GRNN的可见-近红外漫反射光谱检测樱桃糖度的研究

联合使用连续小波变换(continuous wavelet transform,CWT)和广义回归神经网络(generalized regression neural networks,GRNN)建立用于测定樱桃中糖含量的CWT-GRNN 预测校正模型。利用CWT 提取樱桃样本数据中反映含糖量的关键光谱特征,在CWT 域中选择3 个具有代表性的尺度,并在每个尺度下根据樱桃样本的可见- 近红外光谱的特征将其划分为4 个特征区间,从而构造12 个特征输入到GRNN,GRNN 的光滑因子取为0.0001。CWTGRNN模型对20 个预测样本集中的樱桃含糖量的预测相对误差在2% 以内。结果表明,可见- 近红外光谱技术可以快速、准确和无损地测定樱桃中的含糖量,本研究提出的方法可以用于果蔬产业的品质管理与控制。     

近红外光谱大数据分析塔罗科血橙内在品质的研究

可溶性固形物(total soluble solid,TSS)、可滴定酸(titratable acidity,TA)及维生素C(vitamin C,V_C)含量是评价柑橘内在品质及风味的重要特性指标,应用近红外光谱(near infrared spectrum instrument,NIRS)对塔罗科血橙内在品质进行无损、快速检测。利用近红外光谱(NIRS)收集100个塔罗科血橙样品的300个原始光谱数据,处理得到100个平均光谱,其中随机抽取67个作为校正集,33个作为预测集;运用偏最小二乘法(partial least squares,PLS)建立校正与预测模型,再通过实际验证评价建立的PLS模型的性能;试验得到TSS、TA及V_C预测值与实际值的相关系数r值分别是0.833、0.699、0.925;将PLS模型进行实际验证,TSS的差异基本表现在1%以内,占比71.43%;V_C基本表现在5 mg/100 mL以内,占比70.00%。结果表明NIRS可作为一种快速、无损的方法检测塔罗科血橙的内在品质,但TA的相关系数较差,还有待进一步研究。     

软枣猕猴桃总酚的可见-近红外漫反射光谱无损检测

应用可见-近红外漫反射光谱在570～1848nm光谱区域内,建立了软枣猕猴桃总酚定量数学模型。实验将贮藏分三个阶段(采收阶段,贮藏12d,贮藏24d)进行,通过对比分析不同建模方法得到软枣猕猴桃总酚定标模型。结果表明,应用偏最小二乘回归算法、一阶导数处理和反相多元离散校正处理所建软枣猕猴桃总酚定标模型的预测性能较好。定标集样本的交互验证相关系数(RCV)为0.8939,交互验证均方根误差(RMSECV)为11.6734mg/100g;预测集样本的相关系数(RP)为0.8627,预测均方根误差(RMSEP)为16.7690mg/100g。研究表明：可见/近红外漫反射光谱对软枣猕猴桃总酚的快速无损检测具有一定的可行性, 但模型精度有待提高。     

基于可见/近红外高光谱成像技术的鸡蛋新鲜度无损检测

以400~1 000nm高光谱系统获得鸡蛋样本的高光谱图像,利用蒙特卡洛法检测异常样本,采用不同预处理方法处理原始光谱;应用竞争性正自适应加权算法(Competitive Adaptive Reweighted Sampling,CARS)、遗传偏最小二乘法(Genetic Algorithms PLS,GAPLS)和间隔蛙跳法(Interval Random Frog,IRF)对预处理后光谱数据提取特征波长;分别建立基于全光谱和特征波长的偏最小二乘回归(Partial Least Squares Regression,PLSR)和最小二乘支持向量机(Least Squares Support Vector Machine,LS-SVM)鸡蛋新鲜度预测模型。结果表明:标准正态变量变换(Standardized Normal Variate,SNV)法为最优预处理方法;利用CARS、GAPLS和IRF分别选出8,35,74个特征波长;基于GAPLS提取的特征波长的LS-SVM模型最优,其校正相关系数(Rc)为0.899,预测相关系数(Rp)为0.832。表明基于高光谱成像技术的鸡蛋新鲜度无损检测是可行的。     

Nondestructive Measurement of Acidity of Strawberry Using Vis/NIR Spectroscopy

Vis/Near infrared reflectance spectroscopy appears to be a rapid and convenient non-destructive technique that can measure the quality and compositional attributes of many substances. This paper assesses the ability of NIR reflectance spectroscopy to estimate the acidity of strawberry. Spectra were collected from 65 samples and data was expressed as absorbance, the logarithm of the reciprocal of reflectance (log1/R). The absorbance data was subsequently compressed using wavelet transformation. Two models to predict the acidity in strawberry were constructed. A prediction model based on wavelet transform (WT) combined with partial least squares (PLS) was found better with the r of 0.856, RMSEP of 0.026, and in the confidence lever 95%.     

Non-destructive measurement of acidity of Chinese bayberry using Vis/NIRS techniques

Chinese bayberry (Myrica rubra Siebold and Zuccarini) is cultivated in southeast China for its edible fruits. In this research, the potential of using the visible/near infrared spectroscopy (Vis/NIRS) was investigated for measuring the acidity of Chinese bayberry, and the relationship was established between non-destructive Vis/NIRS measurement and the acidity of Chinese bayberry. Intact Chinese bayberry fruit was measured by reflectance Vis/NIR in 325–1075 nm range. The data set as the logarithms of the reflectance reciprocal (absorbance (log 1/R)) was analyzed in order to build the best prediction model for this characteristic, using several spectral pretreatments and multivariate calibration techniques such as partial least square regression (PLS). The model for prediction the acidity (r=0.963), standard error of prediction (SEP) 0.21 with a bias of 0.138; showed an excellent prediction performance. The Vis/NIRS technique has significantly greater accuracy for determining the acidity. This non-destructive, fast and accuracy technology can be used in food industry that would be beneficial to human health.     

Non-destructive discrimination of paddy seeds of different storage age based on Vis/NIR spectroscopy

The potential of visible/near infrared reflectance (Vis/NIR) spectroscopy for non-destructive discrimination of paddy seeds of different storage age was examined based on Vis/NIR spectroscopy coupled with chemometrics. Data from 210 samples of paddy seed were collected from 325 to 1075 nm using a field spectroradiometer. The spectral data were processed and analyzed by chemometrics, which integrated the methods of wavelet transform (WT), principal component analysis (PCA) and artificial neural networks (ANN) modelling. The noise of spectral data was filtered and diagnostic information was extracted by the WT method. Then, diagnostic information from WT was visualized in principal components space, in which the structures with the storage period were discovered. Finally, the first eight principal components, which accounted for 99.94% of the raw spectral variables, were used as the input for the ANN model. A promising model was achieved with a high discrimination accuracy rate of 97.5%. Thus, an effective and non-destructive way to discriminate paddy seeds of different storage periods was put forward.     

近红外漫反射无损检测赣南脐橙中可溶性固形物和总酸

目的:利用近红外漫反射无损检测技术对赣南脐橙可溶性固形物和总酸含量进行相关研究。方法:通过自行设计的NIR光谱系统测定150个赣南脐橙可溶性固形物和总酸。120个赣南脐橙样品用来建模,其余30个用来验证模型的性能。采集完整赣南脐橙的近红外漫反射光谱(350～1800nm),光谱经移动窗口平滑处理、一阶微分和二阶微分预处理后,再分别采用主成分回归(PCR)和偏最小二乘法(PLS),建立赣南脐橙可溶性固形物和总酸含量的定量预测数学模型。结果:采用一阶微分结合偏最小二乘法所建模型的预测效果较好,可溶性固形物和总酸含量定量预测数学模型的相关系数分别为0.9263和0.9562,均方根误差分别为0.4102°Brix和0.018%。结论:近红外漫反射光谱作为一种无损的检测方法,可用于评价赣南脐橙的可溶性固形物和总酸含量。     

可见/近红外光谱半透射法检测苹果中可溶性固形物含量

目的 检验自行搭建的半透射光谱采集平台检测水果中可溶性固形物含量的可行性, 并比较不同光谱采集方式对光谱模型的影响。方法 以红富士苹果为检测对象, 光谱采集平台中的USB2000 光谱仪采集半透射光谱数据, AntarisⅡ FT-NIR光谱仪采集漫反射光谱数据, 同标准法检测得到的苹果可溶性固形物含量建立偏最小二乘(PLS)模型, 并结合不同的预处理方式优化近红外光谱模型。结果 比较发现采用半透射的光谱采集方式优于漫反射方式。半透射光谱采用平滑处理后模型预测性能最佳, 对样本预测得到相关系数为0.937, 均方根误差为0.517。结论 自行搭建的光谱采集平台可行, 为今后检测水果的光谱采集方式提供参考。     

A New Approach to Predict Acidity of Bayberry Juice by Using Vis/Near Infrared Spectroscopy

A total of 76 bayberry juices were collected and their spectra features were got by using a vis/NIR spectroscopy. One mixed algorithm was used to predict the acidity (pH) of bayberry juice with partial least squares (PLS) and artificial neural network (ANN). PLS was used to find some sensitive spectra actives to acidity in juice, before doing this, the influence of various spectral pretreatments (standard normal variate, multiplicative scatter correction, S.Golay first derivative, wavelet package transform) were compared. The PLS approach with WPT preprocessing spectra was found to provide the best results, and the spectral reflectivity corresponding to them were regarded as the input neurons of ANN. Remnant values by subtracting standard values and validation values, were regarded as the output neurons of ANN. The calibration equation developed from them was used to predict the constituent values for the independent spectra of 30 samples. The results indicated that the observed results using PLS-ANN (r_p = 0.943) were better than those obtained by PLS (r_p = 0.932). At the same time, the sensitive wavelengths corresponding to the acidity of bayberry juices or some element at a certain band were proposed on the basis of regression coefficients by PLS. It indicates that using vis/NIRS technique to fast and nondestructive detection the acidity of bayberry juices was feasible.     

Rapid screening of DON contamination in whole wheat meals by Vis/NIR spectroscopy and computer vision coupling technology

This work intends to develop an online experimental system for screening of deoxynivalenol (DON) contamination in whole wheat meals by visible/near-infrared (Vis/NIR) spectroscopy and computer vision coupling technology. Spectral and image information of samples with various DON levels was collected at speed of 0.15 m s⁻¹ on a conveyor belt. The two-type data were then integrated and subjected to chemometric analysis. Discriminant analysis showed that samples could be classified by setting 1000 μg kg⁻¹ as the cut-off value. The best correct classified rate obtained in prediction was 93.55% based on fusion of spectral and image features, with reduced prediction uncertainty as compared to single feature. However, quantification of DON by quantitative analysis was not successful due to poor model performance. These results indicate that, although not accurate enough to provide conclusive result, this coupling technology could be adopted for rapid screening of DON contamination in cereals and feeds during processing.     

Study of golden pompano (Trachinotus ovatus) freshness forecasting method by utilising Vis/NIR spectroscopy combined with electronic nose

Golden pompano (Trachinotus ovatus) quality forecasting method utilising Vis/NIR spectroscopy combined with electronic nose (EN) was investigated in this article. Responses of Vis/NIR spectroscopy and EN to pompanos stored at 4°C were measured for 6 days. Physical/chemical indexes including texture, total volatile basic nitrogen, pH, total viable counts, and human sensory evaluation were synchronously examined as quality references. Chemometric methods including principal component analysis (PCA) and stochastic resonance (SR) were employed for spectroscopic and EN data analysis. Physicochemical examination demonstrated that fish quality decreased rapidly during storage. PCA qualitatively classified freshness degree of pompano samples, while SR signal-to-noise ratio (SNR) spectrum using SNR maximum quantitatively characterised quality for all samples. Golden pompano quality predictive models were developed based on spectroscopy, EN, and spectroscopy combined with EN, respectively. Results demonstrated that the model developed based on spectroscopy combined with EN presented a forecasting accuracy of 93.3%.     

多品种洋梨糖度近红外普适性模型的建立

目的 开发多品种洋梨糖度的普适性模型。方法 采用主成分得分空间距离将5个品种洋梨分为两组: 阿巴特 康佛伦斯 五九香(组1), 凯斯凯德 红考密斯(组2)。分别建立多品种洋梨SSC的普适性模型, 以Q值来评价模型综合性能。结果 组1和组2洋梨SSC的普适性模型具有较好性能, 其Q = 0.849、0.735(PLS)和0.875、0.749(MLR)。结论 多品种洋梨品质的MLR普适性模型可用于便携仪器, 实现现场洋梨SSC精确定量检测。     

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.