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.     

Near-Infrared Spectroscopy for Classification of Oranges and Prediction of the Sugar Content

A nondestructive method for the classification of orange samples according to their growing conditions and geographic areas was developed using Vis/Near infrared spectroscopy. The results showed that the NIR spectra of the samples were moderately clustered in the principle component space and pattern recognition wavelet transform (WT) combined artificial neural network (BP-ANN) provided satisfactory classification results. Additionally, a partial least square (PLS) method was constructed to predict the sugar content of certain oranges. It showed excellent predictions of the sugar content of oranges, with standard error of prediction (SEP) values of 0.290 and 0.301 for Shatangju and Huangyanbendizao, respectively.     

哈姆林甜橙果实内在品质的可见-近红外光谱无损检测法

采用可见- 近红外漫反射光谱技术,结合偏最小二乘法,以不同时间采摘的哈姆林甜橙果实为样品建立其可溶性固形物、含酸量和VC 的无损检测数学模型,同时对不同光谱预处理方法和不同建模波段范围对模型的预测性能进行对比分析。结果表明：原始光谱在400～1000nm 波段的模型预测精度较高。经多元散射校正和5 点移动平均平滑预处理后,果实可溶性固形物含量的PLS 模型最好,校正集样品的相关系数为0.995RMSEC和RMSEP分别为0.026%、0.028%;预测集样品的相关系数为0.992。经多元散射校正和9 点移动平均平滑预处理后,果实含酸量的PLS 模型最好,校正集样品的相关系数为0.997,RMSEC 和RMSEP 分别为0.012%、0.013%;预测集样品的相关系数为0.997。经多元散射校正和9 点移动平均平滑预处理后,果实VC 含量的PLS 模型最好,校正集样品的相关系数为0.998,RMSEC 和RMSEP 分别为0.009%、0.009%;预测集样品的相关系数为0.999。可见由不同时间采摘的果实组成的样品集所建立的数学模型可以提高模型的预测精度,从而提高模型的适用范围。应用可见-近红外漫反射光谱检测哈姆林甜橙果实的内在品质可行。     

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。     

基于多源感知信息融合的牛肉新鲜度分级检测

利用机器视觉和近红外光谱的多源感知信息融合技术评判牛肉新鲜度,并开发了相关的识别系统。以牛后腿肉为研究对象,对获取的图像特征信息和光谱特征信息,采用BP神经网络建立牛肉新鲜度分级模型。其中,通过主成分分析提取相应的主成分因子作为建模输入,根据挥发性盐基氮含量划分新鲜度等级作为模型输出。结果发现,在图像特征信息因子数为6、光谱信息主成分因子数为6时,建立的模型预测识别率可达98.31%。结果表明,基于机器视觉和近红外光谱技术的多源感知信息融合技术评判牛肉新鲜度的方法可行。     

Automatic freshness assessment of cod (Gadus morhua) fillets by Vis/Nir spectroscopy

VIS/NIR spectroscopy for differentiating between fresh and frozen-thawed cod fillets and for assessing the freshness as days on ice has been evaluated. Both a handheld interactance probe for doing quick measurements of single fillets and an imaging spectrometer for doing online analysis at industrial speed of one fillet per second, have been used. Results show that frozen-thawed cod fillets can be fully separated from fresh fillets using a small subset of wavelengths in the visible region. Freshness as days on ice can be determined with an accuracy of 1.6 days on individual fillets. The results indicate that oxidation of hemoglobin and myoglobin during freezing-thawing and cold storage on ice are explaining most of the variations seen in the visible region of the spectrum.     

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.     

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。     

Near infrared hyperspectral imaging in quality and safety evaluation of cereals

The requirements of cereal research, as well as grading and evaluation of food products, have encouraged the development of nondestructive, rapid, and accurate analytical techniques to evaluate grain quality and safety. NIR hyperspectral imaging integrates spectroscopy and imaging techniques in one analytical system, allowing direct identification of chemical components and their distribution within the sample. It is a promising technique that may be implemented on-line, enabling the cereal industry to move away from subjective, manual classification and measuring methods. NIR hyperspectral imaging has gained popularity for rapidly acquiring information to enable the quantification, identification or differentiation of a variety of cereal properties. The technique can potentially replace multiple conventional chemical, microbial or physical tests with a single, automated image acquisition. Individual kernels can be analyzed nondestructively, enabling one to follow changes in the same kernel over time (e.g. fungal development). Although NIR hyperspectral imaging has not been extensively implemented in industry, it shows great potential for the development of an evaluation system to assess cereal grains, especially regarding variety discrimination and grading/classification properties. This review outlines the theory and principles of NIR hyperspectral imaging, and focuses specifically on its application in cereal science research and industry.     

VIS/NIR spectroscopy for non-destructive freshness assessment of Atlantic salmon (Salmo salar L.) fillets

Visible/near-infrared spectroscopy has been evaluated for use in freshness prediction and frozen-thawed classification of farmed Atlantic salmon fillets, where fresh samples were stored as whole fish in ice. A handheld interactance probe for performing rapid measurements of single fillets and an imaging spectrometer for online analysis at an industrial speed of one fillet per second, have been used. Freshness as storage days in ice is predicted with an accuracy of 2.4 days for individual fillets, whereas frozen-thawed salmon fillets are completely separated from fresh fillets. The prediction results are comparable to previous results using the Quality Index Method with trained panelists. The region between 605 and 735 nm, which excludes interference by carotenoids and water, is appropriate for both frozen-thawed classification and freshness prediction of salmon fillets. The results indicate that the spectral changes are explained mainly by oxidation of heme proteins during the freeze–thaw cycle and during chilled storage in ice.     

Nondestructive Spectroscopic and Imaging Techniques for Quality Evaluation and Assessment of Fish and Fish Products

Nowadays, people have increasingly realized the importance of acquiring high quality and nutritional values of fish and fish products in their daily diet. Quality evaluation and assessment are always expected and conducted by using rapid and nondestructive methods in order to satisfy both producers and consumers. During the past two decades, spectroscopic and imaging techniques have been developed to nondestructively estimate and measure quality attributes of fish and fish products. Among these noninvasive methods, visible/near-infrared (VIS/NIR) spectroscopy, computer/machine vision, and hyperspectral imaging have been regarded as powerful and effective analytical tools for fish quality analysis and control. VIS/NIR spectroscopy has been widely applied to determine intrinsic quality characteristics of fish samples, such as moisture, protein, fat, and salt. Computer/machine vision on the other hand mainly focuses on the estimation of external features like color, weight, size, and surface defects. Recently, by incorporating both spectroscopy and imaging techniques in one system, hyperspectral imaging cannot only measure the contents of different quality attributes simultaneously, but also obtain the spatial distribution of such attributes when the quality of fish samples are evaluated and measured. This paper systematically reviews the research advances of these three nondestructive optical techniques in the application of fish quality evaluation and determination and discuss future trends in the developments of nondestructive technologies for further quality characterization in fish and fish products.     

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.     

面粉品质近红外光谱在线检测系统开发与应用

本研究运用近红外光谱无损检测技术,开发了一种适用于面粉品质检测的在线测量系统。本系统在硬件平台基础上,采用C++Builder 6.0对NIR 1.7/S微型光谱仪进行二次开发,编写了具有光谱采集、面粉品质预测、模型更新和数据存储等功能的软件。对市售170种面粉进行试验,以面粉水分含量为代表性指标。通过对比不同光谱预处理方法建模结果,发现不进行任何预处理时的面粉水分偏最小二乘回归(PLS)得到的模型精度最高。建模集和验证集决定系数(R2)分别为0.947,0.841;均方根误差(RMSE)分别为0.146%,0.198%;RPD值为2.53。模型导入软件后对30份新样品进行外部验证,预测值与测量值决定系数(R2)为0.883,均方根误差为0.206%。结果表明,该系统能够初步实现面粉水分的实时预测,为近红外在线检测技术应用提供了一定的技术参考。     

马铃薯品质鉴别的近红外光谱与多源信息耦合

建立了基于机器视觉和近红外光谱技术的分级概率输出,利用DS证椐融合规则,搭建适用于异源数据的无损检测分级决策模型.采用方向梯度直方图和主成分提取方法提取光谱特征,并应用支持向量机和AdaBoost分类器进行识别,在此基础上,构建了基于特征层融合的马铃薯分级模型.采用多源信息融合技术,建立了融合无损检测分级决策和特征层融...