Detection of insect-damaged wheat kernels using near-infrared hyperspectral imaging

Insect damage in wheat adversely affects its quality and is considered one of the most important degrading factors in Canada. The potential of near-infrared (NIR) hyperspectral imaging for the detection of insect-damaged wheat kernels was investigated. Healthy wheat kernels and wheat kernels visibly damaged by Sitophilus oryzae, Rhyzopertha dominica, Cryptolestes ferrugineus, and Tribolium castaneum were scanned in the 1000–1600 nm wavelength range using an NIR hyperspectral imaging system. Dimensionality of the acquired hyperspectral data was reduced using multivariate image analysis. Six statistical image features (maximum, minimum, mean, median, standard deviation, and variance) and 10 histogram features were extracted from images at 1101.69 and 1305.05 nm and given as input to statistical discriminant classifiers (linear, quadratic, and Mahalanobis) for classification. Linear discriminant analysis and quadratic discriminant analysis classifiers correctly classified 85–100% healthy and insect-damaged wheat kernels.     

Feasibility of Vis/NIR spectroscopy for detection of flaws in hazelnut kernels

The feasibility of Vis/NIR spectroscopy for detection of flaws in hazelnut kernels (Corylus avellana L. cv. Tonda Gentile Romana) is demonstrated. Feature datasets comprising raw absorbance values, raw absorbance ratios (Abs[λ₁]:Abs[λ₂]) and differences (Abs[λ₁] − Abs[λ₂]) for all possible pairs of wavelengths from 306.5 nm to 1710.9 nm were extracted from the spectra for use in an iterative LDA routine. For each dataset, several spectral pretreatments were tested. Each group of features selected was subjected to Partial Least Squares Discriminant Analysis (PLS-DA), Receiver Operating Characteristics (ROCs) analysis, and evaluation of performance through the Area Under ROC Curve. The best result (5.4% false negative, 5.0% false positive, 5.2% total error) was obtained using a Savitzky–Golay second derivative on the dataset of raw absorbance differences. The optimal features were Abs[564 nm]–Abs[600 nm], Abs[1223 nm]–Abs[1338 nm] and Abs[1283 nm]–Abs[1338 nm]. The results indicate the feasibility of a rapid, online detection system.     

Detection of fusarium damage in Canadian wheat using visible/near-infrared hyperspectral imaging

Fusarium damage in wheat may reduce the quality and safety of food and feed products. In this study, the use of hyperspectral imaging was investigated to detect fusarium damaged kernels (FDK) in Canadian wheat samples. More than 5,200 kernels, representing seven major Canadian wheat classes, with varying degree of infection symptoms ranging from sound through mild to severe were imaged in the visible-NIR (400–1,000 nm) wavelength range. Partial least squares discriminant analysis (PLS-DA) was used to segregate kernels into sound and damaged categories based on kernel mean spectra. A universal PLS-DA model based on four wavelengths was able to detect FDK in all seven classes with an overall accuracy of 90 % and false positives of 9 %.     

Non-destructive assessment of instrumental and sensory tenderness of lamb meat using NIR hyperspectral imaging

The purpose of this study was to develop and test a hyperspectral imaging system (900–1700 nm) to predict instrumental and sensory tenderness of lamb meat. Warner–Bratzler shear force (WBSF) values and sensory scores by trained panellists were collected as the indicator of instrumental and sensory tenderness, respectively. Partial least squares regression models were developed for predicting instrumental and sensory tenderness with reasonable accuracy (R_cv = 0.84 for WBSF and 0.69 for sensory tenderness). Overall, the results confirmed that the spectral data could become an interesting screening tool to quickly categorise lamb steaks in good (i.e. tender) and bad (i.e. tough) based on WBSF values and sensory scores with overall accuracy of about 94.51% and 91%, respectively. Successive projections algorithm (SPA) was used to select the most important wavelengths for WBSF prediction. Additionally, textural features from Gray Level Co-occurrence Matrix (GLCM) were extracted to determine the correlation between textural features and WBSF values.     

便携式近红外水果糖度分析模型简化研究

为了实现便携式近红外光谱仪水果糖度现场快速分析,将桃、梨和苹果的光谱进行二阶导数和卷积平滑处理后,利用组合移动窗口偏最小二乘法选择信息变量建立PLS模型,利用遗传偏最小二乘法选择信息变量建立MLR模型.分析表明,桃、梨和苹果PLS模型的RMSEP分别为0.417、0.372和0.654,其RSDP分别为4.685%、3.348%和4.111%;MLR模型的RMSEP分别为0.381、0.382和0.550,其RSDP分别为4.281%、3.438%和3.457%,模型预测精度均满足现场检测应用要求.结果表明, 用SCMWPLS和GA-PLS可以提取最有效信息变量,模型更加简洁、数据运算量也更少,模型适用于便携式近红外光谱仪器.     

Non-destructive determination of water-holding capacity in fresh beef by using NIR hyperspectral imaging

This study was carried out for post-mortem non-destructive prediction of water holding capacity (WHC) in fresh beef using near infrared (NIR) hyperspectral imaging. Hyperspectral images were acquired for different beef samples originated from different breeds and different muscles and their spectral signatures were extracted. Both principal component analysis (PCA) and partial least squares regression (PLSR) models were developed to obtain an overview of the systematic spectral variations and to correlate spectral data of beef samples to its real WHC estimated by drip loss method. Partial least squares modeling resulted in a coefficient of determination (R_CV²) of 0.89 and standard error estimated by cross validation (SECV) of 0.26%. The PLSR loadings showed that there are some important absorption peaks throughout the whole spectral range that had the greatest influence on the predictive models. Six wavelengths (940, 997, 1144, 1214, 1342, and 1443 nm) were then chosen as important wavelengths to build a new PLS prediction model. The new model led to a coefficient of determination (R_CV²) of 0.87 and standard error estimated by cross validation (SECV) of 0.28%. Image processing algorithm was then developed to transfer the predicting model to each pixel in the image for visualizing drip loss in all portions of the sample. The results showed that hyperspectral imaging has the potential to predict drip loss non-destructively in a reasonable accuracy and the results could be visualised for identification and classification of beef muscles in a simple way. In addition to realize the difference in WHC within one sample, it was possible to accentuate the difference in samples having different drip loss values.     

Prediction of beef quality attributes using VIS/NIR hyperspectral scattering imaging technique

Hyperspectral imaging images were used to predict fresh beef tenderness (WBSF: Warner-Bratzler Shear Force) and color parameters (L^∗, a^∗, b^∗). Sixty-five fresh strip loin cuts were collected from 33 carcass after 2 days postmortem. After acquiring hyperspectral images, the samples were vacuum packaged and aged for 7 days, and then the color parameters and WBSF of the samples were measured as references. The optical scattering profiles were extracted from the images and fitted to the Lorentzian distribution (LD) function with three parameters. LD parameters, such as the scattering asymptotic vale, the peak height, and full scattering width were determined at each wavelength. Stepwise discrimination was used to identify optimal wavelengths. The LD parameters’ combinations with optimal wavelengths were used to establish multi-linear regression (MLR) models to predict the beef attributes. The models were able to predict beef WBSF with R_cv = 0.91, and color parameters (L^∗, a^∗, b^∗) with R_cv of 0.96, 0.96 and 0.97, respectively.     

Thermal imaging to detect infestation by Cryptolestes ferrugineus inside wheat kernels

Canada's zero tolerance for live insects in grain received from farmers, and shipped to domestic and export buyers, has necessitated the development of an accurate insect detection method. An infrared thermal imaging system was developed to detect infestation by six developmental stages (four larval instars, pupae and adults) of Cryptolestes ferrugineus under the seed coat on the germ of the wheat kernels. The artificially infested wheat kernels were removed from the incubation room (30 °C), refrigerated (5 °C) for 60 s, maintained at ambient conditions for 20 s, and imaged using a thermal camera to identify each developmental stage (n=283). The means of the highest 5% and 10% of all temperature values on the surface of the grain were significantly higher (=0.05) for grains having young larvae inside and lower for grains having pupae inside. Temperature distribution on the surface of the infested kernels with different stages of C. ferrugineus was highly correlated with the respiration rate of each developmental stage (r=0.83–0.91). The overall classification accuracy for a quadratic function was 83.5% and 77.7% for infested and sound kernels, respectively, and for a linear function, it was 77.6% and 83.0% for infested and sound kernels, respectively, in pairwise discriminations. Thermal imaging has the potential to identify whether the grain is infested or not, but is less effective in identifying which developmental stage is present.     

Application of NIR hyperspectral imaging for discrimination of lamb muscles

The potential of near-infrared (NIR) hyperspectral imaging system coupled with multivariate analysis was evaluated for discriminating three types of lamb muscles. Samples from semitendinosus (ST), Longissimus dorsi (LD) and Psoas Major (PM) of Charollais breed were imaged by a pushbroom hyperspectral imaging system with a spectral range of 900-1700 nm. Principal component analysis (PCA) was used for dimensionality reduction, wavelength selection and visualizing hyperspectral data. Six optimal wavelengths (934, 974, 1074, 1141, 1211 and 1308 nm) were selected from the eigenvector plot of PCA and then used for discrimination purpose. The results showed that it was possible to discriminate lamb muscles with overall accuracy of 100% using NIR hyperspectral reflectance spectra. An image processing algorithm was also developed for visualizing classification results in a pixel-wise scale with a high overall accuracy.     

Application of Vis/NIR Spectroscopy for Chinese Liquor Discrimination

As one of the most widely consumed alcoholic beverages, Chinese liquor varies greatly in price, flavor, and quality. This diversity calls for effective and reliable discrimination methods. In an attempt to find the best liquor discrimination method, this study used different methods to analyze and identify 730 Chinese liquor samples including 22 kinds, ten brands, and six flavors. These samples, covering most of the famous liquors in China, were analyzed by visible and near-infrared (Vis/NIR) spectroscopy and modeled by three classification methods including supporting vector machine, soft independent modeling of class analogy, and linear discriminate analysis based on principal component analysis (PCA-LDA). Pretreatments and parameters for each model were optimized, and models discrimination ability was compared. The research finds that PCA-LDA was the best model with an average prediction rate of 98.94 % in the training set and 95.70 % in the test set. The correct rates for brands, flavor styles, ages, and alcohol degrees were all higher than 95 %. It shows that Vis/NIR is a reliable, inexpensive, and effective tool for Chinese liquors discrimination.     

Scanning electron microscopy of Fusarium damaged kernels of spring wheat

Kernels of five wheat cultivars (Triticum aestivum) of different bread-making quality were examined. Grown under field conditions, heads of wheat were inoculated in the flowering stage with an aqueous suspension of Fusarium culmorum conidia. Wheat heads were collected from the control and inoculated plots at full maturity. Control (non-inoculated) kernels without any symptoms of disease and Fusarium damaged kernels (FDK) were examined under scanning electron microscopy (SEM). Examination of the FDK fraction confirmed localisation of Fusarium hyphae on the surface and inside the tissues of kernels. Observations of the endosperm from Fusarium infected kernels revealed presence of fungal hyphae in the endosperm and some characteristic structural changes in many of its regions, such as partial or complete lack of the protein matrix, damage to large and small starch granules caused by fungal amylolytic enzymes, disappearance of small starch granules as the colonisation progressed, complete disappearance of the starchy endosperm under severe infection. Fungal colonisation of the endosperm and structural changes in its area were highly variable traits within the FDK fraction of a given cultivar.     

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%.     

Measurement of yogurt internal quality through using Vis/NIR spectroscopy

A nondestructive optical method for determining the sugar content and acidity of yogurt was investigated. Three types of preprocessing, S. Golay smoothing with multiplicative scatter correction (S. Golay smoothing with MSC), S. Golay 1st-Der and wavelet package transform (WPT), were used before the data were analyzed with chemometrics methods of partial least square (PLS). Spectral data sets as the logarithms of the reflectance reciprocal were analyzed to build a best model for predicting the sugar content and acidity of yogurt. A model using preprocessing of WPT with a correlation coefficient of 0.91 and 0.90, a root mean square error of prediction (RMSEP) of 0.36 and 0.04 showed an excellent prediction performance to sugar content and acidity. S. Golay smoothing with MSC was also finer, combined with the calibration and validation results. S. Golay 1st-Der was the worse preprocessing method in this experiment. In the paper, a multivariate calibration method of principal component artificial neural network (PC-ANN) was also established. In PC-ANN models, the scores of the principal components were chosen as the input nodes for the input layer of ANN. After adjusting the number of input nodes (principal components), hidden nodes, as well as learning rate and momentum of the network, a model with a correlation coefficient of 0.92 and 0.91, a root mean square error of prediction (RMSEP) of 0.33 and 0.04 showed an excellent prediction performance on sugar content and acidity. At the same time, the sensitive wavelengths corresponding to the sugar content and acidity of yogurt were proposed on the basis of regression coefficients by PLS.     

Using a Short Wavelength Infrared (SWIR) hyperspectral imaging system to predict alpha amylase activity in individual Canadian western wheat kernels

Sprout damage (pre-harvest germination) in wheat results in highly deleterious effects on end-product quality. Alpha-amylase, the pre-dominant enzyme in the early stage of sprouting has the most damaging effect. This paper introduces a new method using a SWIR hyperspectral imaging system (1000–2500 nm) to predict the α-amylase activity of individual wheat kernels. Two classes of Canadian wheat, Canada Western Red Spring (CWRS) and Canada Western Amber Durum (CWAD), with samples of differing degrees of sprout damage were investigated. Individual kernels were first imaged with the hyperspectral imaging system and then the α-amylase activity of each kernel was determined analytically. Individual kernel α-amylase activity prediction was significant (R ² 0.54 and 0.73) for CWAD and CWRS, respectively using Partial Least Square regression on the hyperspectral data. A classification method is proposed to separate CWRS kernels with high α-amylase activity level from those with low α-amylase activity giving an accuracy of above 80%. This work shows that hyper/multi-spectral imaging techniques can be used for rapidly predicting the α-amylase activity of individual kernels, detecting sprouting at early stage.     

Quality classification of cooked, sliced turkey hams using NIR hyperspectral imaging system

This study was carried out to develop a hyperspectral imaging system in the near infrared (NIR) region (900-1700 nm) to assess the quality of cooked turkey hams of different ingredients and processing parameters. Hyperspectral images were acquired for ham slices originated from each quality grade and then their spectral data were extracted. Spectral data were analyzed using principal component analysis (PCA) to reduce the high dimensionality of the data and for selecting some important wavelengths. Out of 241 wavelengths, only eight wavelengths (980, 1061, 1141, 1174, 1215, 1325, 1436 and 1641 nm) were selected as the optimum wavelengths for the classification and characterization of turkey hams. The data analysis showed that it is possible to separate different quality turkey hams with few numbers of wavelengths on the basis of their chemical composition. The results revealed the potentiality of NIR hyperspectral imaging as an objective and non-destructive method for the authentication and classification of cooked turkey ham slices.     

Prediction of moisture,color and pH in cooked,pre-sliced turkey hams by NIR hyperspectral imaging system

The investigation was conducted to develop a hyperspectral imaging system in the near infrared (NIR) region (900–1700 nm) to predict the moisture content, pH and color in cooked, pre-sliced turkey hams. Hyperspectral images were acquired by scanning the ham slices (900–1700 nm) originated from different quality grade of turkey hams. Spectral data were then extracted and analyzed using partial least-squares (PLSs) regression, as a multivariate calibration method, to reduce the high dimensionality of the data and to correlate the NIR reflectance spectra with quality attributes of the samples considered. Instead of using a wide range of spectra, the number of wavebands was reduced for more stable, comprehensive and faster model in the subsequent multispectral imaging system. From this point of view, important wavelengths were selected to improve the predictive power of the calibration models as well as to simplify the model by avoiding repetition of information or redundancies. With the help of PLS regression analysis, nine wavelengths (927, 944, 1004, 1058, 1108, 1212, 1259, 1362 and 1406 nm) were selected as the optimum wavelengths for moisture prediction, eight wavelengths (927, 947, 1004, 1071, 1121, 1255, 1312 and 1641 nm) for pH prediction and nine wavelengths (914, 931, 991, 1115, 1164, 1218, 1282, 1362 and 1638 nm) were identified for color (a^*) prediction. With the identified reduced number wavelengths, good coefficients of determination (R²) of 0.88, 0.81 and 0.74 with RMSECV of 2.51, 0.02 and 0.35 for moisture, pH and color, respectively, were achieved, reflecting reasonable accuracy and robustness of the models.     

基于VIS/NIR高光谱散射特征预测牛肉的嫩度

本文研究利用VIR/NIR光谱散射特征预测成熟 7 天牛肉的嫩度.开发高光谱散射成像系统,获取新鲜牛肉 400～1100 nm 波长范围高光谱散射图像,对牛肉嫩度进行预测和分级.利用洛伦兹函数,拟合各个波长处的散射曲线,获取不同波长散射曲线的洛伦兹分布函数参数.使用逐步回归方法,选择最佳波长及相应的拟合参数,建立线性回归模型预测牛肉的嫩度,使用全交叉验证方法评价模型的性能.使用散射曲线的峰值建立的模型对嫩度的预测结果最好,预测相关系数为0.86,预测残差为11.7 N.以嫩度剪切力值 58.8 N 为界将牛肉分为粗糙牛肉组和嫩牛肉组,对嫩度的分级准确率是 91%.该研究表明,利用牛肉的散射特征可以对牛肉嫩度预测和分级.     

Prediction of moisture content uniformity of microwave-vacuum dried mangoes as affected by different shapes using NIR hyperspectral imaging

The prediction of moisture content uniformity on mango slices as affected by four different shapes (square, rectangle, regular triangle, and round shape) during microwave-vacuum drying (MVD) was investigated using near-infrared hyperspectral imaging in combination with multivariate chemometric analysis. Applying spectral pretreatment of a 2nd derivative followed by mean-center to raw spectra was found to be greatly beneficial for the reduction of noise and scattering levels. Seven wavelengths (951, 977, 1138, 1362, 1386, 1420, and 1440 nm) with larger absolute values of regression coefficients derived from a partial least square regression model were identified as feature variables for moisture prediction. An optimized model based on the selected wavelengths was developed using multivariate linear regression, achieving a high prediction accuracy with R_p² = 0.993 and RMSEP = 1.282%. From the moisture distribution map, a similar non-uniform drying pattern was found on square, rectangle and regular triangle-shaped samples, while round-shaped mango slices achieved better drying results.Industrial relevanceThe current study suggested that NIR hyperspectral imaging was a promising technique in predicting the moisture content of mango slices during MVD, and non-uniformity of moisture distribution and the effect of sample geometry should be taken into account when the microwave-vacuum method is implemented in drying.     

Classification of in-shell peanut kernels nondestructively using VIS/NIR reflectance spectroscopy

One of the grading factors for peanuts is their classification into peanuts with good or bad kernels. Traditional manual methods are labor intensive and subjective. A device by which the classification could be done rapidly and without the need to shell the peanuts would be very useful for the peanut industry. In this work VIS/NIR spectroscopy was used for this purpose. Reflectance spectra were collected for peanut pods (in-shell peanuts) in the wavelength range of 400–2500 nm. A calibration group of about 200 pods were initially scanned to train the classification algorithm. Each individual pod was shelled and the kernels were visually examined and classified as bad if they had any kind of damage, discoloration or immaturity. The remaining pods were marked as good ones. The Principal component analysis model generated from primary spectra with or without pretreatments gave explained variance better than 99%. The maximum normalization model with the ability of characterizing good and bad kernels with an accuracy of 80% and with low SEP and RMSEP values of 0.43, would be useful in the quality characterization of in-shell peanuts.     

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.