Detection and prediction of Sitophilus oryzae infestations in triticale via visible and near-infrared spectral signatures

Triticale is a hybrid of wheat and rye grown for use as animal feed. In Florida, due to its soft coat, triticale is highly vulnerable to Sitophilus oryzae L. (rice weevil) and there is interest in development of methods to detect early-instar larvae so that infestations can be targeted before they become economically damaging. The objective of this study was to develop prediction models of the infestation degree for triticale seed infested with rice weevils of different growth stages. Spectral signatures were tested as a method to detect rice weevils in triticale seed. Groups of seeds at 11 different levels (degrees) of infestation, 0–62%, were obtained by combining different ratios of infested and uninfested seeds. A spectrophotometer was used to measure reflectance between 400 and 2500 nm wavelength for seeds that had been infested at different levels with six different growth stages from egg to adult. The reflectance data were analyzed by several generalized linear regression and classification methods. Different degrees of infestation were particularly well correlated with reflectances in the 400–409 nm range and other wavelengths up to 967 nm, although later growth stages could be detected more accurately than early infestation. Stepwise variable selection produced the lowest mean square differences and yielded a high R² value (0.988) for the 4th instars, pupae and adults inside the seed. Models were developed to predict the level of infestation in triticale by rice weevils of different growth stages. Overall, this study showed a great potential of using reflectance spectral signatures for detection of the level of infestation of triticale seed by rice weevils of different growth stages.     

Quality evaluation of pickling cucumbers using hyperspectral reflectance and transmittance imaging: Part I. Development of a prototype

This article reports on the development of a hyperspectral imaging prototype for online evaluation of external and internal quality of pickling cucumbers. The prototype consisted of a two-lane round belt conveyor, two illumination sources (one for reflectance and one for transmittance), and a hyperspectral imaging unit. It had a novel feature of simultaneous imaging under reflectance mode in the visible region (400–675 nm) and transmittance mode for the red and near-infrared region (Red-NIR) (675–1000 nm). Reflectance information from the visible region was intended for evaluating the external characteristics of cucumbers such as skin color, whereas transmittance information from Red-NIR was used for internal defect detection (i.e., hollow center). Additional features of the prototype included simultaneous acquisition of reflectance and transmittance from calibration references that were installed in the system, to provide real-time, continuous corrections of individual hyperspectral images from each sample. Methods and algorithms were developed of estimating cucumber fruit size and correcting the effect of fruit size on transmittance measurements. The system was calibrated and evaluated for detecting the color, size, and internal defect of pickling cucumbers.     

Quality evaluation of pickling cucumbers using hyperspectral reflectance and transmittance imaging—Part II. Performance of a prototype

This paper reports on the development and evaluation of methods and algorithms for detecting both external and internal quality of pickling cucumbers using the hyperspectral reflectance and transmittance images acquired by an online prototype described in a previous paper [1]. Experiments were performed in 2 years on ‘Journey’ pickling cucumbers, some of which were subjected to mechanical stress to induce internal defect in seed cavity. Hyperspectral images of the ‘Journey’ pickling cucumbers were collected under reflectance, transmittance, and their combination modes. Partial least squares analysis was performed on spectra extracted from the hyperspectral images to predict firmness, color, and the presence of internal defect. The system performed well on predicting skin color (chroma and hue) with the coefficient of determination (R ²) ranging between 0.75 and 0.77; however, it had poor prediction of fruit firmness. Transmittance data in the spectral region of 675–1,000 nm provided the best detection of internal defect for the test pickling cucumbers, with the detection accuracy up to 99%. Up to the best four wavelength combinations were identified using linear discriminant analysis for internal defect detection. The hyperspectral imaging technique can be used for simultaneous detection of color, size, and internal defect on pickling cucumbers.     

Comparison of different modes of visible and near-infrared spectroscopy for detecting internal insect infestation in jujubes

This study compared the abilities of the interactance, reflectance, and transmission modes of visible and near-infrared (Vis/NIR) spectroscopy in detecting internal insect-infested jujubes. Statistical analysis was performed to identify the effective wavelengths that best discriminated the insect-infested jujubes from intact jujubes and to derive a discriminant function in classifying the jujubes showing internal insect infestation and those that were free of infestation. The highest correct classification rates obtained from the above modes were 100%, 90.0%, and 97.0%, respectively. The interactance mode in the long-wave NIR (LWNIR) range is preferable to the transmission mode in the visible and short-wave near-infrared (VSWNIR) ranges. Furthermore, the transmission mode in the VSWNIR range displayed an obvious advantage over the reflectance mode in every range. The results indicate that it is possible to use both the interactance and transmission modes to develop a system in detecting the internal qualities of jujubes.     

Evaluation of Least Squares Support Vector Machine Regression and other Multivariate Calibrations in Determination of Internal Attributes of Tea Beverages

This research aimed to explore the relationship between internal attributes (pH and soluble solids content) of tea beverages and diffuse reflectance spectra. Three multivariate calibrations including least squares support vector machine regression (LSSVR), partial least squares (PLS), and radial basis function (RBF) neural network were adopted for development of internal attributes determination models. Ten kinds of tea beverages including green tea and black tea were selected for visible and near infrared reflectance (Vis/NIR) spectroscopy measurement from 325 to 1,075 nm. As regard the kernel function, least squares–support vector machine regression models were built with both linear and RBF kernel functions. Grid research and tenfold cross-validation procedures were adopted for optimization of LSSVR parameters. The generalization ability of LSSVR models were evaluated by adjusting the number of samples in the training set and testing set, and sensitive wavelengths that were closely correlated with the internal attributes were explored by analyzing the regression coefficients from linear LSSVR model. Excellent LSSVR models were built with r = 0.998, standard error of prediction (SEP) = 0.111, for pH and r = 0.997, SEP = 0.256, for soluble solids content, and it can be found that the LSSVR models outperformed the PLS and RBF neural network models with higher accuracy and lower error. Six individual sensitive wavelengths for pH were obtained, and the corresponding pH determination model was developed with r = 0.994, SEP = 0.173, based on these six wavelengths. The soluble solids content determination model was also developed with r = 0.977, SEP = 0.173, based on seven individual sensitive wavelengths. The above results proved that Vis/NIR spectroscopy could be used to measure the pH and soluble solids content in tea beverages nondestructively, and LSSVR was an effective arithmetic for multivariate calibration regression and sensitive wavelengths selection.     

Application of Near-Infrared Hyperspectral Imaging for Detection of External Insect Infestations on Jujube Fruit

A hyperspectral imaging system has been built for detecting external insect damage and acquiring reflectance images from jujubes in the near-infrared region of 900–1700 nm. Spectral information was extracted from each jujube, and six optimal wavelengths (987, 1028, 1160, 1231, 1285, and 1464 nm) were obtained using principal component analysis. The first principal component images (PC-1) using the selected six wavelengths were obtained for further image processing. The detection algorithm was then developed based on principal component analysis and two-band ratio (R1160/R1464) coupled with image subtraction algorithm (R1160-R1464). An identification accuracy of 93.1% for insect-infested jujubes and 100% classification rate for the intact ones were achieved. The results of this research demonstrated that it is feasible to discriminate insect-infested jujubes from intact jujubes using the near-infrared hyperspectral imaging technology.     

Application of near infrared spectroscopy to peanut grading and quality analysis: overview

Techniques using near infrared (NIR) spectroscopy for quality measurements are becoming more popular in food processing and quality inspection of agricultural commodities. NIR spectroscopy has several advantages over conventional physical and chemical analytical methods of food quality analysis. It is a rapid and non destructive method and provides more information about the components and its structure present in the food products. It can measure more than one parameter simultaneously. The NIR spectrum includes wavelengths from 750 to 3000 nm that follow immediately after the visible region (400–700 nm). Many organic compounds can be well-defined by NIR reflectance, transmittance or diffuse reflectance system. This paper reviews the application of NIR spectroscopy to several oil seeds and examines the feasibility of using this technique for peanut quality analysis. The NIR spectroscopic instrumentation has been explained briefly for a better understanding. Also needs and limitations in use of NIR spectroscopy for peanut quality analysis and grading were explained.     

Identification of wheat classes at different moisture levels using near-infrared hyperspectral images of bulk samples

Wheat classes at different moisture levels need to be identified to accurately segregate, properly dry, and safely store before processing. This paper introduces a new method using a near infrared (NIR) hyperspectral imaging system (960–1,700 nm) to identify five western Canadian wheat classes (Canada Western Red Spring (CWRS), Canada Western Extra Strong (CWES), Canada Western Red Winter (CWRW), Canada Western Soft White Spring (CWSWS), and Canada Western Hard White Spring (CWHWS)) and moisture levels, independent of each other. The objectives of this research also included identification of each wheat class at specific moisture levels of 12, 14, 16, 18, and 20%. Bulk samples of wheat were scanned in the 960–1,700 nm wavelength region at 10 nm intervals using an Indium Gallium Arsenide (InGaAs) NIR camera. Spectral feature data sets were developed by calculating relative reflectance intensities of the scanned images. Principal components analysis was used to generate scores images and loadings plots. The NIR wavelengths in the region of 1,260–1,360 nm were important based on the loadings plot of first principal component. In statistical classification, the linear and quadratic discriminant analyses were used to classify wheat classes giving accuracies of 61–97 and 82–99%, respectively, independent of moisture contents. It was also found that the linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) could classify moisture contents with classification accuracies of 89–91 and 91–99%, respectively, independent of wheat classes. Once wheat classes were identified, classification accuracies of 90–100 and 72–99% were observed using LDA and QDA, respectively, when identifying specific moisture levels. Spectral features at key wavelengths of 1,060, 1,090, 1,340, and 1,450 nm were ranked at top in classifying wheat classes with different moisture contents. This work shows that hyperspectral imaging techniques can be used for rapidly identifying the wheat classes even at varying moisture levels.     

Citrus canker detection using hyperspectral reflectance imaging and PCA-based image classification method

Citrus canker is one of the most devastating diseases that threaten marketability of citrus crops. Technologies that can efficiently identify citrus canker would assure fruit quality and safety and enhance the competitiveness and profitability of the citrus industry. This research was aimed to investigate the potential of using hyperspectral imaging technique for detecting canker lesions on citrus fruit. A portable hyperspectral imaging system consisting of an automatic sample handling unit, a light source, and a hyperspectral imaging unit was developed for citrus canker detection. The imaging system was used to acquire reflectance images from citrus samples in the wavelength range between 400 and 900 nm. Ruby Red grapefruits with normal and various diseased skin conditions including canker, copper burn, greasy spot, wind scar, cake melanose, and specular melanose were tested. Hyperspectral reflectance images were analyzed using principal component analysis (PCA) to compress the 3-D hyperspectral image data and extract useful image features that could be used to discriminate cankerous samples from normal and other diseased samples. Image processing and classification algorithms were developed based upon the transformed images of PCA. The overall accuracy for canker detection was 92.7%. Four optimal wavelengths (553, 677, 718, and 858 nm) were identified in visible and short-wavelength near-infrared region that could be adopted by a future multispectral imaging solution for detecting citrus canker on a sorting machine. This research demonstrated that hyperspectral imaging technique could be used for discriminating citrus canker from other confounding diseases.     

Optical absorption and scattering properties of normal and defective pickling cucumbers for 700�C1000?nm

An effective optical inspection system for detecting defective pickling cucumbers is needed to help the pickle industry deliver consistent pickled products to the consumer. This research was intended to measure the optical absorption and scattering properties of normal and internally defective pickling cucumbers, using hyperspectral imaging-based spatially-resolved technique. Spatially-resolved hyperspectral scattering images were acquired from 50 freshly harvested ‘Journey’ pickling cucumbers in the summer of 2008 before they were subjected to rolling under mechanical load to induce internal damage. The damaged cucumbers were imaged 1 h and 1 day after the mechanical stress treatment. Spectra of the absorption and reduced scattering coefficients for pickling cucumbers were extracted from the spatially-resolved scattering profiles, using an inverse algorithm for a diffusion theory model, for the spectral range of 700–1000 nm. It was found that within 1 h after mechanical damage, changes in the absorption and reduced scattering coefficients for the cucumbers were minimal. One day after mechanical damage, the absorption coefficient for the cucumbers increased significantly (at 5–10% level) for the wavelengths of 700–920 nm, whereas the reduced scattering coefficient decreased significantly for the wavelengths of 700–1000 nm (at 10% level). Overall mechanical damage caused greater changes in absolute value to the scattering properties than to the absorption properties. This research suggests that effective defect detection can be achieved by enhancing scattering characteristics measurement in the optical evaluation of pickling cucumbers.     

Detection of Deoxynivalenol Using Fluorescence Excitation–Emission Matrix

Deoxynivalenol (DON) is an acutely toxic mycotoxin produced in wheat and other cereal grains when exposed to fungi such as Fusarium spp. In this study, DON aqueous samples at seven concentration levels were examined using the excitation–emission matrix (EEM). EEM is a graph composed of an excitation wavelength axis, an emission wavelength axis, and a fluorescence intensity axis. It is acquired by measuring the fluorescence intensity of a sample at consecutive excitation and emission wavelengths. The EEM of DON solution showed fluorescent peaks, which were nonexistent in that of water, particularly in the excitation (Ex) and emission (Em) wavelength ranges: Ex 200–240 nm/Em 300 nm and Ex 250–300 nm/Em 400–500 nm. Since the EEM is a multidimensional data composed of many wavelength conditions (667 in this case), principal component analysis was applied to reduce the data to lower dimensions. By plotting the scores of the first and second principal components, it was shown that DON could be discriminated from water and the concentration of DON in the aqueous solution could be judged.     

Detection of External and Internal Insect Infestation in Wheat by Near-Infrared Reflectance Spectroscopy

Near-infrared spectroscopy (NIR) in the reflectance mode was investigated for the rapid, automatic and non-destructive detection of insect stored-grain pests external or internal to wheat kernels. Convincing calibration performance was obtained for external adult Oryzaephilus surinamensis (L) (saw-toothed grain beetle) in unmilled samples including varieties Beaver (soft wheat) and Mercia (hard wheat) at two moisture contents. With this large substrate variability, the method could differentiate between uninfested samples and samples with approximately 270 insects kg⁻¹ or more. Milling made no improvement. Large spectral differences were observed between uninfested kernels and kernels infested internally with Sitophilus granarius (L) (grain weevil) larvae or pupae, arising from both a changed chemical composition and physical structure. Single uninfested and infested kernels were discriminated by their second derivative (d²) spectra. For both external and internal infestation there was substantial evidence that insect protein and/or chitin and moisture were being detected. NIR should be useful as a rapid method of detection.     

Combining multispectral reflectance and fluorescence imaging for identifying bruises and stem-end/calyx regions on Golden Delicious apples

As a part of the project for detecting bruises on Golden Delicious apple using vision system, the present paper shows a method that could separate the stem-end/calyx regions from the true bruises by combining the information of hyperspectral reflectance and fluorescence images. The images were scanned between 400 nm and 1,000 nm with a hyperspectral imaging system. Different light sources were constructed for capturing the reflectance and fluorescent images. Compared to the reflectance signal, the fluorescence signatures are much less intense, so that only the fluorescence of chlorophyll waveband was further examined (i.e., 685 nm). The analysis showed that the Principal Components scores images, which were based on the reflectance images, can be used for separating the bruised areas as well as the stem-end/calyx regions from the sound apple tissues; whereas only the stem-end/calyx was able to be recognized from the fluorescence images. For the samples investigated in this study, no stem-end/calyx regions were misrecognized as bruises; however, about 12% of bruised surfaces were misclassified as stem-end or calyx regions. All of the healthy tissues were correctly recognized as non-stem-end/calyx regions. The classification results indicated that combining multispectral reflectance and fluorescence imaging may help to identify the stem-end/calyx regions from the true bruised tissue and therefore to improve the accuracy for bruise detection on Golden Delicious apples.     

Chemical profile and antioxidant capacities of tart cherry products

The levels of anthocyanins and other flavonoids, as well as melatonin, in various tart cherry products (frozen and dried cherries, powders from individually quick frozen (IQF) cherry and juice concentrate) from two tart cherry cultivars, ‘Montmorency’ and ‘Balaton’ were analysed comparatively by HPLC and electrospray mass spectrometry (EMS). Our results show that the major anthocyanin compound in these two tart cherry cultivars is cyanidin 3-glucosylrutinoside, followed by cyanidin 3-rutinoside, cyanydin sophoroside, and peonidin 3-glucoside. Studies on antioxidant activities (total antioxidant status assay) of crude extracts of ten tart cherry products show that these products preserve their antioxidant capacities after processing and storage. We have also compared the antioxidant activities of several single constituents that are present in tart cherry. When TEAC (trolox equivalent antioxidant capacity) values were evaluated conceptually against the cherry phytochemical profile, cyanidin and its derivatives were found to be the significant contributors to the antioxidant systems of tart cherries. It was shown that standard compounds with common aglycon moieties show similar antioxidant activities.     

基于可见/近红外光谱技术的橘小实蝇侵染柑橘检测研究

目的 使用可见/近红外光谱技术实施橘小实蝇侵染柑橘不同时期的无损检测。方法 研究选取人工制备的不同侵染时期的柑橘样本作为研究对象,利用搭建的可见/近红外光谱系统测量的光谱信息结合人工标定的侵染时期,对原始光谱进行了5种预处理,采用竞争性自适应重加权算法（competitive adaptive reweighted sampling,CARS）、连续投影算法（successive projection salgorithm,SPA）两类方法提取反映侵染柑橘时期变化的光谱特征波长,应用偏最小二乘判别分析(partial least squares discriminant analysis,PLS-DA)建立基于特征波长光谱的柑橘侵染时期分类模型,对比分析不同光谱预处理方法的模型分类效果。结果 原始光谱经多元散射校正（multiplicative scatter correction,MSC）预处理的模型分类效果最佳,分别经CARS方法和SPA方法优选出了34和16个光谱特征波长。采用MSC-CARS-PLS-DA方法构建的模型分类效果最好,总准确率假阳率分别达到了96.8%和100.0%,模型对健康柑橘和侵染柑橘有较强的分类能力。结论 通过可见/近红外光谱结合PLS-DA判别方法,可以实现橘小实蝇侵染柑橘的无损检测,为今后继续开展橘小实蝇侵染柑橘光谱检测研究提供参考依据。     

利用反射和半透射高光谱图像检测水蜜桃早期冷害

研究了应用高光谱图像技术检测‘霞晖5号’水蜜桃早期冷害的方法。通过分析桃果实采后贮藏期间出汁率的变化,确定冷害发生的初始时间。进而针对早期冷害桃果实的400～1 000 nm波段的高光谱反射和半透射图像,应用独立主成分分析方法优选出冷害的特征波长,反射条件下为656、674 nm和704 nm,半透射条件下为640、745 nm和811 nm,并通过提取每个特征波长下的光谱平均值作为Fisher判别方法建模的特征集,所建水蜜桃早期冷害判别模型的预测准确率分别为83.0%和94.0%。结果表明,高光谱技术可以检测桃果实早期冷害,且半透射照射方式的判别结果优于反射方式。     

Hyperspectral Imaging-Based Classification and Wavebands Selection for Internal Defect Detection of Pickling Cucumbers

Hyperspectral imaging is useful for detecting internal defects of pickling cucumbers. The technique, however, is not yet suitable for high-speed online implementation due to the challenges in analyzing large-scale hyperspectral images. This research aimed to select the optimal wavebands from the hyperspectral image data, so that they can be deployed in either a hyperspectral or multispectral imaging-based inspection system for the automatic detection of internal defects of pickling cucumbers. Hyperspectral reflectance (400–700 nm) and transmittance (700–1,000 nm) images were acquired, using an in-house developed hyperspectral imaging system running at two conveyor speeds of 85 and 165 mm/s, for 300 “Journey” pickling cucumbers before and after internal damage was induced by mechanical load. Minimum redundancy–maximum relevance (MRMR) was used for optimal wavebands selection, and the loadings of principal component analysis (PCA) were also applied for qualitatively identifying the important wavebands that are related to the specific features. Discriminant analysis with Mahalanobis distance classifier was performed for the two-class (i.e., normal and defective) and three-class (i.e., normal, slightly defective, and severely defective) classifications using the mean spectra and textural features (energy and variance) from the regions of interest in the spectral images at selected waveband ratios. The classification results based on MRMR wavebands selection were generally better than those from PCA-based classifications. The two-band ratio of 887/837 nm from MRMR gave the best overall classification results, with the accuracy of 95.1 and 94.2 % at the conveyor speeds of 85 and 165 mm/s, respectively, for the two-class classification. The highest classification accuracies for the three-class classification based on the optimal two-band ratio of 887/837 nm were 82.8 and 81.3 % at the conveyor speeds of 85 and 165 mm/s, respectively. The mean spectra-based classification achieved better results than the textural feature-based classification, except in the three-class classification for the higher conveyor speed. The overall classification accuracies for all selected waveband ratios at the low conveyor speed were slightly higher than those at the higher conveyor speed, since the low speed resulted in more scan lines, thus higher spatial resolution hyperspectral images. The identified two-band ratio of 887/837 nm in transmittance mode could be applied for fast real-time internal defect detection of pickling cucumbers.     

Processed Tart Cherry Products—Comparative Phytochemical Content,in vitro Antioxidant Capacity and in vitro Anti‐inflammatory Activity

Abstract: Processing of fruits and vegetables affects their phytochemical and nutrient content. Tart cherries are commercially promoted to possess antioxidant and anti‐inflammatory activity. However, processing affects their phytochemical content and may affect their related health benefits. The current study compares the in vitro antioxidant capacity and anti‐inflammatory cyclooxygenase activity of processed tart cherry (Prunus cerasus) products—cherry juice concentrate, individually quick‐frozen cherries, canned cherries, and dried cherries. Cherry products were analyzed for total anthocyanin and proanthocyanidin content and profile. On a per serving basis, total anthocyanins were highest in frozen cherries and total proanthocyanidins were highest in juice concentrate. Total phenolics were highest in juice concentrate. Juice concentrate had the highest oxygen radical absorbance capacity (ORAC) and peroxynitrite radical averting capacity (NORAC). Dried cherries had the highest hydroxyl radical averting capacity (HORAC) and superoxide radical averting capacity (SORAC). Processed tart cherry products compared very favorably to the U.S. Dept. of Agriculture‐reported ORAC of other fresh and processed fruits. Inhibition of in vitro inflammatory COX‐1 activity was greatest in juice concentrate. In summary, all processed tart cherry products possessed antioxidant and anti‐inflammatory activity, but processing differentially affected phytochemical content and in vitro bioactivity. On a per serving basis, juice concentrate was superior to other tart cherry products. Practical Application: Processing of fruits and vegetables affects their chemical and nutrient content and perhaps their related health benefits. Comparative studies are valuable to ascertain the effects of processing on fruit and vegetable chemical content and bioactivity. Here we present tart cherries as a model for this type of detailed comparison.     

FRUIT QUALITY OF COLD STORED SWEET CHERRIES INFESTED WITH CODLING MOTH

The effect of codling moth, Cydia pomonella (L.) (Lepidoptera: Tortricidae), infestation on the quality of sweet cherry, Prunus avium (L.) L., fruit was studied. Each cherry was infested with one first instar codling moth and held at 3.3C for 0 (control), 1, 2, 4, 7, 10, or 14 days. After cold storage, fruits were maintained at 25C and observed periodically for damage using a visual scale from 0 as no damage, to 9 as complete destruction. Two groups of ‘Bing’ cherries were examined, one from an organically grown orchard and the other from a commercial orchard with conventional chemical control. Uninfested fruits maintained quality for one month at 25C whereas infested fruits had a damage rating of 2 by the fifth day. Infested organic fruits deteriorated faster than the conventional cherries. Larvae were often not detected because of fruit deterioration and fungal contamination. Fruit quality deterioration due to codling moth infestation is an important component of the Systems Approach for quarantine security for cherries exported to Japan. Infested fruit would rapidly deteriorate as the result of decay and be culled at time of packing.     

Discrimination of Kernel Quality Characteristics for Sunflower Seeds Based on Multispectral Imaging Approach

Multispectral imaging in the visible and near-infrared (405–970 nm) regions was tested for nondestructive discrimination of insect-infested, moldy, heterochromatic, and rancidity in sunflower seeds. An excellent classification (accuracy >97 %) for intact sunflower seeds could be achieved using Fisher’s linear discriminant function based on 10 feature wavelengths that were selected from the original 19 wavelengths by Wilks’ lambda stepwise method. Intact sunflower seeds with different degree of rancidity could be precisely clustered by multispectral imaging technology combined with principal component analysis-cluster analysis (PCA-CA). Our results demonstrate the capability of multispectral imaging technology as a tool for rapid and nondestructive analysis of seed quality attributes, which enables many applications in the agriculture and food industry.