Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Real-Time Systems for High-Throughput Applications <s> A new method for the selection of wavelengths from near infrared spectra using partial least squares(PLS) analysis is presented. The method aims to find wavelengths that produce significant improvements in PLS prediction accuracy over using all wavelengths. The method is based on data splitting and evaluation of the appropriate prediction errors. Analysis of interactance spectra of kiwifruit using three evaluation criteria are compared with the results obtained from full spectrum analysis and with the recently proposed feature selection method. Using the recommended criterion, the method was found to produce models with lower standard errors than the optimum model obtained using the feature selection method 87% of the time. Properties of initiating the search method from starting points selected by three procedures are compared and recommendations are given for selecting the initial wavelengths. The new search method also has a low probability of obtaining significant correlations through chance. <s> BIB001 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Real-Time Systems for High-Throughput Applications <s> Highlights? Aflatoxin contaminated food items can be detected by multispectral imaging. ? Aflatoxin level of the food lot can be decreased by elimination of the detected ones. ? Discriminative features in images were extracted by 3D LDB based algorithm. ? Fusion in data level describes the characteristics of multispectral imaging system. Mycotoxins are the toxic metabolites of certain filamentous fungi and have been demonstrated to cause various health problems in humans, including immunosuppression and cancer. Among them, the aflatoxins have received greater attention because they are potent carcinogens and are responsible for many human deaths per annum, mostly in non-industrialized countries. Various regulatory agencies have enforced limits on the concentrations of these toxins in foods and feeds involved in international commerce. Hyperspectral and multispectral imaging are becoming increasingly important for rapid and nondestructive testing for the presence of such contaminants. However, the high number of spectral bands needed may render such image acquisition systems too complex, expensive and slow. Moreover, they tend to generate overwhelming amount of data, making effective processing of this information in real time difficult. In this study, a two-dimensional local discriminant bases algorithm was developed to detect the location of the discriminative features in the multispectral data space. The algorithm identifies the optimal passband width and center frequencies of optical filters to be used for a multispectral imaging system. This was applied to a multispectral imaging system used to detect aflatoxin-contaminated hazelnut kernels and red chili peppers. Classification accuracies of 92.3% and 80% were achieved for aflatoxin-contaminated and uncontaminated hazelnuts and red chili peppers, respectively. The aflatoxin concentrations were decreased from 608 to 0.84ppb for tested hazelnuts and from 38.26 to 22.85ppb for red chili peppers by removal of the nuts/peppers that were classified as aflatoxin-contaminated. The algorithm was also used to classify fungal contaminated and uncontaminated hazelnut kernels, and an accuracy of 95.6% was achieved for this broader classification. <s> BIB002 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Real-Time Systems for High-Throughput Applications <s> In multispectral imaging, the spectrum is divided into multiple bands, enabling materials or objects in a scene to be identified from the characteristics of their absorption or reflection spectra. This approach permits many applications, including remote sensing1, vegetation mapping2, non-invasive biological imaging3, food quality control4 and face recognition5. Multispectral imaging systems generally use motorized filter wheels6, multiple cameras7, line-scanning8 or tunable filters9 to obtain images in multiple spectral bands. Despite this wide range of uses, relatively few commercial systems are available and most are complicated, expensive and bulky. To address these limitations, several approaches based on filter arrays have been proposed10,11. An array of containing four NIR filter functions was demonstrated12. Because these filters were dielectric interference designs, however, the fabrication process was relatively complicated as the deposition of multiple dielectric layers was required for the realization of each filter function. Furthermore, this process had to be repeated to achieve the four filter functions. Plasmonic structures have also been proposed for colour filtering13,14,15,16. Their optical characteristics have been measured but, to the best of our knowledge, no imaging experiments have been performed. <s> BIB003 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Real-Time Systems for High-Throughput Applications <s> The performance of a multispectral imaging system has been evaluated in monitoring aerobically packaged beef filet spoilage at different storage temperatures (0, 4, 8, 12, and 16°C). Spectral data in the visible and short wave near infrared area (405-970nm) were collected from the surface of meat samples and correlated with microbiological data (log counts), for total viable counts (TVCs), Pseudomonas spp., and Brochothrix thermosphacta. Qualitative analysis (PLS-DA) was employed for the discrimination of meat samples in three microbiological quality classes based on the values of total viable counts, namely Class 1 (TVC<5.5log10CFU/g), Class 2 (5.5log10CFU/g7.0log10CFU/g). Furthermore, PLS regression models were developed to provide quantitative estimations of microbial counts during meat storage. In both cases model validation was implemented with independent experiments at intermediate storage temperatures (2 and 10°C) using different batches of meat. Results demonstrated good performance in classifying meat samples with overall correct classification rate for the three quality classes ranging from 91.8% to 80.0% for model calibration and validation, respectively. For quantitative estimation, the calculated regression coefficients between observed and estimated counts ranged within 0.90-0.93 and 0.78-0.86 for model development and validation, respectively, depending on the microorganism. Moreover, the calculated average deviation between observations and estimations was 11.6%, 13.6%, and 16.7% for Pseudomonas spp., B. thermosphacta, and TVC, respectively. The results indicated that multispectral vision technology has significant potential as a rapid and non-destructive technique in assessing the microbiological quality of beef fillets. <s> BIB004 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Real-Time Systems for High-Throughput Applications <s> Abstract The objective of this study was to evaluate the safety quality of wheat kernels and more specifically the infection of wheat by Fusarium culmorum. We have developed a new non-destructive methodology based on multispectral imaging and chemometrics to detect wheat contamination. This method has been applied to evaluate the tolerance to Fusarium Head Blight (FHB) on six accessions of durum wheat. Two of the accessions are parental lines, one susceptible and one resistant to Fusarium, and the other four are their offspring lines. Multispectral images of dorsal and ventral sides of all kernels were acquired with an in-house imaging system and processed with chemometrical tools, including principal component analysis (PCA) and multiple linear regression (MLR). According to the quantitative PCR (qPCR) results, PCA was able to detect infested kernels and to identify affected areas with kernels. Moreover, regression analysis of the images allowed the degree of contamination for each pixel to be estimated, and thus mapping of contamination for each kernel. <s> BIB005 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Real-Time Systems for High-Throughput Applications <s> Recently, machine vision is gaining attention in food science as well as in food industry concerning food quality assessment and monitoring. Into the framework of implementation of Process Analytical Technology (PAT) in the food industry, image processing can be used not only in estimation and even prediction of food quality but also in detection of adulteration. Towards these applications on food science, we present here a novel methodology for automated image analysis of several kinds of food products e.g. meat, vanilla creme and table olives, so as to increase objectivity, data reproducibility, low cost information extraction and faster quality assessment, without human intervention. Image processing’s outcome will be propagated to the downstream analysis. The developed multispectral image processing method is based on unsupervised machine learning approach (Gaussian Mixture Models) and a novel unsupervised scheme of spectral band selection for segmentation process optimization. Through the evaluation we prove its efficiency and robustness against the currently available semi-manual software, showing that the developed method is a high throughput approach appropriate for massive data extraction from food samples. <s> BIB006 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Real-Time Systems for High-Throughput Applications <s> The practical value of a criterion based on statistical information theory is demonstrated for the selection of optimal wavelength and bandwidth of low-cost lighting systems in plant imaging applications. Kullback---Leibler divergence is applied to the problem of spectral band reduction from hyperspectral imaging. The results are illustrated on various plant imaging problems and show similar results to the one obtained with state-of-the-art criteria. A specific interest of the proposed approach is to offer the possibility to integrate technological constraints in the optimization of the spectral bands selected. <s> BIB007 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Real-Time Systems for High-Throughput Applications <s> Monitoring of tablet quality attributes in direct vicinity of the production process requires analytical techniques that allow fast, non-destructive, and accurate tablet characterization. The overall objective of this study was to investigate the applicability of multispectral UV imaging as a reliable, rapid technique for estimation of the tablet API content and tablet hardness, as well as determination of tablet intactness and the tablet surface density profile. One of the aims was to establish an image analysis approach based on multivariate image analysis and pattern recognition to evaluate the potential of UV imaging for automatized quality control of tablets with respect to their intactness and surface density profile. Various tablets of different composition and different quality regarding their API content, radial tensile strength, intactness, and surface density profile were prepared using an eccentric as well as a rotary tablet press at compression pressures from 20MPa up to 410MPa. It was found, that UV imaging can provide both, relevant information on chemical and physical tablet attributes. The tablet API content and radial tensile strength could be estimated by UV imaging combined with partial least squares analysis. Furthermore, an image analysis routine was developed and successfully applied to the UV images that provided qualitative information on physical tablet surface properties such as intactness and surface density profiles, as well as quantitative information on variations in the surface density. In conclusion, this study demonstrates that UV imaging combined with image analysis is an effective and non-destructive method to determine chemical and physical quality attributes of tablets and is a promising approach for (near) real-time monitoring of the tablet compaction process and formulation optimization purposes. <s> BIB008 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Real-Time Systems for High-Throughput Applications <s> Abstract A near-infrared (NIR) hyperspectral imaging (HSI) system was used to predict viability and vigor (in term of germination periods) in muskmelon seeds. Hyperspectral images of muskmelon seeds were acquired using a NIR push-broom HSI system covering the spectral range of 948–2494 nm. After NIR spectra collection, all seeds underwent a germination test to confirm their viability and vigor. The spectra from seeds with 3 and 5 germination days and nongerminated seeds were further used for development of a classification model of partial least-squares discriminant analysis (PLSDA). Most effective wavelengths were selected using three model-based variable selection methods, i.e., variable important in projection (VIP), selectivity ratio (SR), and significance multivariate correlation (sMC), which selected 23, 18, and 19 optimal variables, respectively, from full set of 208 variables. The selected variables from different waveband selection methods were found genuine and significant for interpreting the prediction results of seed viability and vigor. Subsequently, the PLS-DA model was constructed using individual VIP-, SR-, or sMC-selected variables. The results demonstrated that the PLSDA model developed with the selected optimal variables from the different methods provided comparable results for the calibration set; however, the PLSDA-SR method afforded the highest classification accuracy (94.6%) for a validation set used to determine the viability and vigor of muskmelon seeds. The wavelengths selected by the different methods represents chemical components of the seed and the attribute of germination ability was chosen most often. <s> BIB009
The main challenge of seed industry in the recent century is to acquire consistent information about overall seed quality and safety during the production, processing, handling, marketing and distribution chains, to satisfy consumer demands and the requirement for market expansion. During the last couple of decades, the advancement in image acquisition protocols along with rapid image processing algorithms has permitted quantifications of many morphological and physico-chemical features of the inspected seeds. Conventional methods for measuring seed quality traits by visual inspection of seed intactness and checking germination capacity, seed health and freedom from disease infestation are criticized as being costly and tedious analytical procedures. In this context, imaging techniques, especially spectral imaging methods, have been proved to be efficient techniques for rapid monitoring of numerous seed quality traits. Thence, it was very critical for seed community to explore the potential of this technique in real-time implementations BIB008 . Because hyperspectral cameras are expensive and not suited for high-throughput testing, the development of more simple and affordable devices is necessary BIB005 . Actually, most of the research works in using hyperspectral imaging system were basically conducted in the hope of developing a multispectral imaging system that could be implemented in real-time applications for predicting and visualizing various intrinsic or morphological features. Many published articles claimed to have already developed multispectral imaging systems for prediction and visualization of certain quality attributes but very few of these works were crowned with a robust design that could be installed in on-line scenarios. Such research attempts focused on scanning seed samples in laboratory-based hyperspectral imaging systems followed by selecting some key wavelengths most related to certain quality features without using such wavelengths in building an independent and fully-functional multispectral imaging system. Although the process of selecting the ideal wavelengths using various machine learning routines is substantially important, the main challenge is to utilize such wavelengths in establishing a multispectral machine vision system. Despite this wide range of usages of multispectral imaging systems, few systems are commercially available in the market and most of them are complicated, expensive and bulky BIB003 . Thus, it is extremely important to develop a computer-integrated system that has the ability to deliver a large amount of seeds to the imaging system (e.g., through a conveying system) and subsequently sort the seeds into different fractions using robotic picking or some kind of pneumatic or mechanical gate system  . The timely detection of seed quality attributes that do not meet quality specification standards is advantageous, as it allows immediate rejection of the imported lots, thereby saving time and money. Furthermore, seed quality evaluation directly after production and drying process is necessary, because it allows continuous control of the major quality parameters. In addition, timely determination of seed viability, intactness and wholesomeness is required for process control, as the occurrence of defect-causing features such as fungal infection or disease infestation are strongly dependent on the seed health conditions. In general, the real-time or near real-time assessment of quality attributes of seeds and/or any relevant bio-product in harsh environment requires a better understanding of the critical quality attributes of the seeds being analyzed. Also, a balance between the cost of equipment including hardware and software, the detection speed of the imaging system, the detection accuracy of the model, and the service life of the equipment must be all considered during building a comprehensive real-time spectral imaging system to move the operations the from the laboratory setting to online systems in the industry BIB008 . Because the development of an automated system for real-time evaluation of seed quality during all stages of the production chains in very essential, this system should be readily available to the industry and easy-to-use without requiring special expertise from end-users. In addition, the system must be accurate and reliable for providing rapid, non-destructive, low cost analysis with minimum or no sample preparation and have the potential to analyze multiple attributes simultaneously BIB004 . Although the developed system may be automated, their implementation on seed applications could be not straightforward if the relevant wavelengths have not been carefully selected or when the calibration procedure has not been precisely conducted BIB006 . Consequently, the procedure of selecting the 'optimal' wavelength is considered the most important step in developing and establishing an a high-throughput system that has the capability of performing the inspection and quality control decision in real time BIB007 . Indeed, the real-time operations are very complicated processes that require acquisition of images at several bands and process such images simultaneously. The procedure of selecting the ideal wavelengths can either be performed before the modeling or even during the modeling process by discarding the unnecessary wavelengths that add complexity of the model and reduce its efficiency in classification or prediction. These unwanted wavelengths make model interpretation difficult and affect the classification and prediction efficiency of the model BIB009 . There are several methods for selecting such wavelengths BIB001 ; but there is no only one magic method that fits all applications. The method of wavelength selection should also identify the optical center frequency and bandwidth (FWHM) that provide optimal discrimination in the multispectral imaging system BIB002 . By using the optimal wavelengths and the appropriate modeling algorithm, the system will allow the visualization of various attributes within the sample because regions with similar spectral patterns tend to appear in the same colour which facilitates its separation and categorization by the system. Moreover, using fewer wavebands in multispectral imaging designs can lead to faster sensor systems, thus reducing total integration time and increasing the number of samples inspected per second BIB009 .