Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Chemometrics and Spectral Analysis <s> Damage is an important quality factor for grading, marketing, and end use of soybean. Seed damage can be caused ::: by weather, fungi, insects, artificial drying, and by mechanical damage during harvest, transportation, storage, and handling. ::: The current visual method for identifying damaged soybean seeds is based on discoloration and is subjective. The objective ::: of this research was to classify sound and damaged soybean seeds and discriminate among various types of damage using ::: NIR spectroscopy. A diode–array NIR spectrometer, which measured reflectance spectra (log[1/R]) from 400 to 1,700 nm, ::: was used to collect single–seed spectra. Partial least square (PLS) models and neural network models were developed to ::: classify sound and damaged seeds. For PLS models, the NIR wavelength region of 490 to 1,690 nm provided the highest ::: classification accuracy for both cross–validation of the calibration sample set and prediction of the validation sample set. ::: Classification accuracy of sound and damaged soybean seeds was higher than 99% when using a two–class model. The ::: classification accuracies of sound seeds and those damaged by weather, frost, sprout, heat, and mold were 90%, 61%, 72%, ::: 54%, 84%, and 86%, respectively, when using a six–class model. Neural network models yielded higher classification ::: accuracy than PLS models. The classification accuracies of the validation sample set were 100%, 98%, 97%, 64%, 97%, and ::: 83% for sound seeds and those damaged by weather, frost, sprout, heat, and mold, respectively, for the neural network model. ::: The optimum parameters of the neural network model were learning rate of 0.7 and momentum of 0.6. <s> BIB001 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Chemometrics and Spectral Analysis <s> Variations in the quality of wheat kernels can be an important problem in the cereal industry. In particular, desiccation conditions play an essential role in both the technological characteristics of the kernel and its ability to sprout. In planta desiccation constitutes a key stage in the determinism of the functional properties of seeds. The impact of desiccation on the endosperm texture of seed is presented in this work. A simple imaging system had previously been developed to acquire multivariate images to characterize the heterogeneity of food materials. A special algorithm for the use under principal component analysis (PCA) was developed to process the acquired multivariate images. Wheat grains were collected at physiological maturity, and were subjected to two types of drying conditions that induced different kinetics of water loss. A data set containing 24 images (dimensioned 702 × 524 pixels) corresponding to the different desiccation stages of wheat kernels was acquired at different wavelengths and then analyzed. A comparison of the images of kernel sections highlighted changes in kernel texture as a function of their drying conditions. Slow drying led to a floury texture, whereas fast drying caused a glassy texture. The automated imaging system thus developed is sufficiently rapid and economical to enable the characterization in large collections of grain texture as a function of time and water content. <s> BIB002 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Chemometrics and Spectral Analysis <s> The effects of the number of seeds in a training sample set on the ability to predict the viability of cabbage or radish seeds are presented and discussed. The supervised classification method extended canonical variates analysis (ECVA) was used to develop a classification model. Calibration sub-sets of different sizes were chosen randomly with several iterations and using the spectral-based sample selection algorithms DUPLEX and CADEX. An independent test set was used to validate the developed classification models. The results showed that 200 seeds were optimal in a calibration set for both cabbage and radish data. The misclassification rates at optimal sample size were 8%, 6% and 7% for cabbage and 3%, 3% and 2% for radish respectively for random method (averaged for 10 iterations), DUPLEX and CADEX algorithms. This was similar to the misclassification rate of 6% and 2% for cabbage and radish obtained using all 600 seeds in the calibration set. Thus, the number of seeds in the calibration set can be re... <s> BIB003 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Chemometrics and Spectral Analysis <s> In the durum wheat industry, the milling process leads to the production of different fractions such as semolina, flours, bran and thirds. The histological origin and composition of these fractions are not easy to determine. Moreover, the proportions of kernel tissue in a given fraction depend on the wheat cultivar. In the present work, 4 batches of durum wheat were processed in an experimental semolina pilot plant leading to the extraction of 18 representative fractions. Multispectral images of both the final fractions and wheat kernels have been acquired with an in-house imaging system. By performing principal component analysis and a Kmeans procedure on the multispectral images of each fraction, a reference spectral fingerprint was assessed for every fraction. For each pixel of the images of kernel cross-section, it was possible to calculate its probability of belonging to a typical group of fingerprint, and then to build up a probability image. These probability images, presented in false colors, emphasized the areas in the kernels from which a given milled fraction was extracted. The images obtained in this way enable a more precise identification of the histological origin of the different milled fractions. <s> BIB004 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Chemometrics and Spectral Analysis <s> The requirements of reliability, expeditiousness, accuracy, consistency, and simplicity for quality assessment of food products encouraged the development of non-destructive technologies to meet the demands of consumers to obtain superior food qualities. Hyperspectral imaging is one of the most promising techniques currently investigated for quality evaluation purposes in numerous sorts of applications. The main advantage of the hyperspectral imaging system is its aptitude to incorporate both spectroscopy and imaging techniques not only to make a direct assessment of different components simultaneously but also to locate the spatial distribution of such components in the tested products. Associated with multivariate analysis protocols, hyperspectral imaging shows a convinced attitude to be dominated in food authentication and analysis in future. The marvellous potential of the hyperspectral imaging technique as a non-destructive tool has driven the development of more sophisticated hyperspectral imaging systems in food applications. The aim of this review is to give detailed outlines about the theory and principles of hyperspectral imaging and to focus primarily on its applications in the field of quality evaluation of agro-food products as well as its future applicability in modern food industries and research. <s> BIB005 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Chemometrics and Spectral Analysis <s> Abstract The current US corn grading system accounts for the portion of damaged kernels, measured by time-consuming and inaccurate visual inspection. Near infrared spectroscopy (NIRS), a non-destructive and fast analytical method, was tested as a tool for discriminating corn kernels with heat and frost damage. Four classification algorithms were utilized: Partial least squares discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), k-nearest neighbors (K-NN), and least-squares support vector machines (LS-SVM). The feasibility of NIRS for discriminating normal or viable-germinating corn kernels and soybean seeds from abnormal or dead seeds was also tested. This application could be highly valuable for seed breeders and germplasm-preservation managers because current viability tests are based on a destructive method where the seed is germinated. Heat-damaged corn kernels were best discriminated by PLS-DA, with 99% accuracy. The discrimination of frost-damaged corn kernels was not possible. Discrimination of non-viable seeds from viable also was not possible. Since previous results in the literature contradict the current damage-discrimination results, the threshold of seed damage necessary for NIRS detection should be analyzed in the future. NIRS may accurately classify seeds based on changes due to damage, without any correlation with germination. <s> BIB006 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Chemometrics and Spectral Analysis <s> Crop-to-crop transgene flow may affect the seed purity of non-transgenic rice varieties, resulting in unwanted biosafety consequences. The feasibility of a rapid and nondestructive determination of transgenic rice seeds from its non-transgenic counterparts was examined by using multispectral imaging system combined with chemometric data analysis. Principal component analysis (PCA), partial least squares discriminant analysis (PLSDA), least squares-support vector machines (LS-SVM), and PCA-back propagation neural network (PCA-BPNN) methods were applied to classify rice seeds according to their genetic origins. The results demonstrated that clear differences between non-transgenic and transgenic rice seeds could be easily visualized with the nondestructive determination method developed through this study and an excellent classification (up to 100% with LS-SVM model) can be achieved. It is concluded that multispectral imaging together with chemometric data analysis is a promising technique to identify transgenic rice seeds with high efficiency, providing bright prospects for future applications. <s> BIB007 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Chemometrics and Spectral Analysis <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> BIB008 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Chemometrics and Spectral Analysis <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> BIB009 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Chemometrics and Spectral Analysis <s> A simulation method for approximating spectral signatures of minced meat samples was developed depending on concentrations and optical properties of the major chemical constituents. Minced beef samples of different compositions scanned on a near-infrared spectroscopy and on a hyperspectral imaging system were examined. Chemical composition determined heuristically and optical properties collected from authenticated references were simulated to approximate samples' spectral signatures. In short-wave infrared range, the resulting spectrum equals the sum of the absorption of three individual absorbers, that is, water, protein, and fat. By assuming homogeneous distributions of the main chromophores in the mince samples, the obtained absorption spectra are found to be a linear combination of the absorption spectra of the major chromophores present in the sample. Results revealed that developed models were good enough to derive spectral signatures of minced meat samples with a reasonable level of robustness of a high agreement index value more than 0.90 and ratio of performance to deviation more than 1.4. <s> BIB010 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Chemometrics and Spectral Analysis <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> BIB011 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Chemometrics and Spectral Analysis <s> Abstract The current detection methods for maize seed damage and viability assessment are usually time-consuming, tedious, and costly. In this investigation, near infrared spectroscopy (NIRS), a nondestructive and rapid analytical method, was explored to analyze frost-damaged and non-viable seeds. Principal component analysis (PCA), partial least squares (PLS) and orthogonal linear discriminant analysis (OLDA) were combined to extract feature from near infrared (NIR) spectra. PLS + OLDA can extract difference characteristics of normal and frost-damaged seeds more efficiently than PCA + OLDA. Three classification algorithms were utilized and compared: Support vector machine (SVM), biomimetic pattern recognition (BPR), and mahalanobis distance (MD). BPR can classify normal and frost-damaged seeds better and achieved the highest average accuracy of 97%. Discrimination model of viable and non-viable seeds in frost-damaged seeds based on SVM, BPR and MD were built and achieved accuracies of 94%, 97.25% and 89.5% respectively. BPR model yielded the most correct value in predicting germination rate of validation set, and improved the germination rate of validation set dramatically from 27.5% to 100% by screening out non-viable seeds. NIRS and chemometrics as demonstrated in this paper can provide a novel method which can assess and improve quality of maize seed quickly and inexpensively. <s> BIB012 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Chemometrics and Spectral Analysis <s> Author Summary: The feasibility of rapid and non-destructive classification of five different tomato seed cultivars was investigated by using visible and short-wave near infrared (Vis-NIR) spectra combined with chemometric approaches. Vis-NIR spectra containing 19 different wavelengths ranging from 375 nm to 970 nm were extracted from multispectral images of tomato seeds. Principal component analysis (PCA) was used for data exploration, while partial least squares discriminant analysis (PLS-DA) and support vector machine discriminant analysis (SVM-DA) were used to classify the five different tomato cultivars. The results showed very good classification accuracy for two independent test sets ranging from 94% to 100% for all tomato cultivars irrespective of chemometric methods. The overall classification error rates were 3.2% and 0.4% for the PLS-DA and SVM-DA calibration models, respectively. The results indicate that Vis-NIR spectra have the potential to be used for non-destructive discrimination of tomato seed cultivars with an opportunity to integrate them into plant genetic resource management, plant variety protection or registration programmes. <s> BIB013 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Chemometrics and Spectral Analysis <s> Image analysis involving mathematical, statistical and software programming approaches are the essential elements of any computer-integrated hyperspectral imaging systems. The theoretical and practical issues associated with the development, analysis, and application of essential image processing algorithms are explored in order to exploit hyperspectral imaging for application to food quality evaluations. The breadth of different image processing approaches adopted over the years in attempting to implement hyperspectral imaging for food quality monitoring was surveyed. Firstly, the fundamental configurations and working principles of hyperspectral systems, as well as the basic concept and structure of hyperspectral data, were described and explained. The understanding of different approaches used during image acquisition, data collection and visualisation were examined. Strategies and essential image processing routines necessary for making the appropriate decision during detection, classification, identification, quantification and/or prediction processes are presented. Examples and figures were selected to reinforce the main approach of each analysis algorithm applied in different agro-food products to answer the question “What does the user want to see in the target food samples?” The theoretical background for each algorithm was beyond the scope of this article thus only essential equations were addressed. The literature presented clearly revealed that hyperspectral imaging systems have gained a rapid interest from researchers to display the chemical structure and related physical properties of numerous types of food stuffs and hyperspectral imaging systems are expected to gain more considerably more potential and application in food processing and engineering plants. <s> BIB014 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Chemometrics and Spectral Analysis <s> Abstract Single seed near-infrared (NIR) spectroscopy is a non-destructive technology commonly used for predicting lipids, proteins, carbohydrates and water content of agricultural products. The aim of the current study is to investigate the prospects of NIR spectroscopy in classifying viable and non-viable tomato seeds of two cultivars using chemometrics. The data exploration were performed by principal component analysis (PCA). Subsequently, viable and non-viable seeds were classified by partial least squares-discriminant analysis (PLS-DA) and interval PLS-DA ( i PLS-DA). The indication of clustering of viable and non-viable seeds were observed in the PCA of each cultivar and the pooled samples. However, the PCA did not exhibit a pattern of separation among the early, normal and late germinated tomato seeds. The NIR spectral regions of 1160–1170, 1383–1397, 1647–1666, 1860–1884 and 1915–1940 nm were identified as important for classification of viable and non-viable tomato seeds by i PLS-DA. The sensitivity i.e. ability to correctly identify the positive samples and specificity i.e. ability to reject the negative samples of the ( i PLS-DA) model on identified spectral regions for prediction of viable and non-viable seeds were 0.94 and 0.94 respectively and were higher than from (PLS-DA) model on original spectra. The i PLS-DA model predicted samples with classification error rate of 6.29 percent as compared to the 13.10 percent by the PLS-DA. The study indicated NIR regions related to the protein-bound water, protein and carbohydrates have a positive relationship to viability in tomato seeds. The study shows the potential of NIR spectroscopy for non-destructive discrimination of viable and non-viable tomato seeds using spectral information. <s> BIB015 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Chemometrics and Spectral Analysis <s> Abstract In recent years, detection of fraudulent and deceptive practices has become a major priority in the food industry and inspection authorities. The aim of this study was to investigate the potential of multispectral imaging coupled with data analysis methods for the detection of minced beef adulteration with horsemeat, as well as to explore model performance during storage in refrigerated conditions. For this reason, multispectral images of 110 samples from three different batches of minced beef and horsemeat in 18 wavelengths were acquired. Images were taken again after samples were stored at 4 °C for 6, 24 and 48 h. Classification models (partial least squares discriminant analysis, random forest, support vector machines) based on the first two batches were developed while the third batch was set aside for external/independent validation. Results showed that freshly-ground and stored samples were clearly distinguishable, whereas classification model performance for detection of adulterated samples was significantly affected by changes in meat color during storage. Using a two-step SVM model however, all pure and freshly-groundsamples were classified correctly and the overall correct classification was equal to 95.31% for independent batch validation. <s> BIB016 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Chemometrics and Spectral Analysis <s> ABSTRACT The potential of multi-spectral visible-near infrared imaging to discriminate beef meat muscles in relation with their type and animal origin was examined in the present study. Two hundred forty muscles of three types (longissimus thoracis, biceps femoris, and semimembranosus) were obtained from the carcasses of three types of animals, two late-maturing cattle types of animals (Limousin and Blond d’Aquitaine) that grow slowly and deposit more muscles and less fat, compared to one early-maturing cattle types of animals (Angus) which tends to have muscles richer in collagen and in intramuscular fat. Two hundred forty cube images were collected with nineteen Ligth Emitting Diodes (405 to 1050 nm) using the Videometer Lab2 device. The image cubes were processed in order to extract image mean spectra and image shape features from co-occurrence and difference of histogram matrices. The results of the partial least square discriminant analysis performed on image texture features and spectral data show a maximum ranging from 63.5 to 83% of good classification depending on the muscle and breed considered. This study demonstrated the promising potential of the visible-near infrared multi-spectral imager to characterize beef meat muscles based on muscle type and its animal origin. <s> BIB017 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Chemometrics and Spectral Analysis <s> Abstract Knowledge of the viability status of seeds before sowing is important to farmers (for yield prediction) and to seed companies (for seed warrant determination). However, a diversity of factors collaborate to reduce or completely render seeds non-viable both during pre- and post-harvest operations. Many methods have been employed to detect seed viability, but perhaps one of the promising is hyperspectral imaging. This is because of its high speed and ability to non-destructively detect the internal conditions of seeds, making it the perfect solution especially for industrial sorting applications. This study was conducted to determine suitable classification model(s) for classifying corn seeds based on their viability using hyperspectral imaging. For this study, 600 corn samples were selected, and half of them treated using microwave heat treatment while the rest were kept as the control group. Hyperspectral imaging data from all the samples were then collected using a shortwave infrared hyperspectral camera with a range of 1000–2500 nm. Three classification models, linear discriminant analysis (LDA), partial least squares discriminant analysis (PLS-DA), and support vector machines (SVM), coupled with some pre-processing methods, were tested to determine the most suitable among them. The SVM model resulted in the highest spectral classification of up to 100%, which is 5% better than the previous research PLS based method. The model also produced flawless classification images, suggesting that hyperspectral imaging can be used to accurately classify corn based on viability. In summary, the results of this study serve as a major step towards development of a fast and non-destructive large-scale hyperspectral-based sorting system for corn viability determination. <s> BIB018
The acquired multispectral images are very abundant in information in the spatial and spectral domains, so that bias-free methods for data mining from the acquired images are very crucial, In recent years, great attention has been paid to this type of LED illumination-based multispectral imaging system due to its robustness, rapid controlled switching ability and cost effectiveness BIB004 BIB008 . The accessibility to different colour and high intensity LEDs of peak wavelengths in the ultraviolet, visible and infrared regions facilitates the construction of cost effective illumination-based multispectral imaging systems. To ensure the highest possible reproducibility of images and to guarantee reliable extracted information, the whole system especially the camera and illumination units should be appropriately calibrated before acquiring the images by adjusting intensity of the reflected light to avoid sensor saturation. Like all modern imaging techniques, even minor variations in the exact setup of light source, physical geometry, sensor sensitivity and imaging method can affect the data quality and hence the ability to apply robust models that give the required information to evaluate the target samples BIB002 . Moreover, the overall calibration procedure is very crucial since any variations in environmental conditions, such as temperature and/or relative humidity, can cause the camera to lose calibration. The acquired multispectral images are very abundant in information in the spatial and spectral domains, so that bias-free methods for data mining from the acquired images are very crucial, especially if one desires to analysis several components simultaneously BIB009 . These multispectral images provide a detailed record of the spectral variation over heterogeneous samples, to which statistical methods could be applied to reveal differences among and within samples and to obtain an overview of the underlying patterns that would not be 'seen' otherwise. Analyzing complex spectra having relatively weak or overlapping bands poses a great challenge BIB011 . When many combinations of variables are involved such as different wavelengths and different seed quality traits, the generated data are immense which implies some challenges in thoroughly interpreting and analyzing the data. In such situations, chemometric data analysis can underpin the dominant patterns in large data matrices in a fast and robust manner. The term 'chemometrics' is originally applied in chemistry, where huge data matrices are generated by developing mathematical and statistical methods to extract useful information related to the chemical characteristics of the analyzed seeds. Although the multispectral images contains very useful information about absorption bands related to overtone and combination vibrations of C-H, N-H and O-H functional groups of the major chemical constituents of the tested seeds, dealing with all of this information is a challenge itself [27] . Data extracted from images can be used to describe differences among samples (e.g., variety discrimination and/or seed health detection) or to predict quality parameters such as chemical combustion (proteins, lipids, moisture, etc.). The transition from images to data models helps in reducing the redundant information involved in the images to other forms of information relevant for specific applications. Based on the purpose of analyses, there are many calibration algorithms and pattern recognition methods that are widely known to perform well for qualitative (classification) and quantitative (prediction/regression) analyses of seed quality. The classification modeling method could be also classified into supervised or unsupervised methods. The supervised techniques are used to build classification models to classify new unknown samples to predefined known classes on the basis of their spectral signatures; meanwhile, the unsupervised methods do not require a prior knowledge about the grouping of the samples BIB005 . Despite the methods used for classification or prediction scenarios, the developed models for multispectral data could be linear such as partial least squares regression 'PLSR' BIB012 BIB001 , partial least squares-discriminant analysis 'PLS-DA' BIB006 BIB015 BIB007 , normalized canonical discriminant analysis 'nCDA'  , extended canonical variates analysis 'ECVA'  BIB003  ; or nonlinear such as support vector machine 'SVM' BIB012 BIB018 BIB013 BIB016 , k-nearest neighbors algorithm 'k-NN' BIB006  and back-propagation neural network 'BPNN' BIB007 . Once a calibration model is developed, it must be properly validated by using the model in predicting quality traits in independent seed samples, not related with samples included during calibration step BIB006 . However, due to light scattering, baseline shift, instrumental drift or path length differences, the spectral data from seeds may exhibit unwanted systematic variations that are not associated with their intrinsic chemical composition. Hence, before extracting any information related to the seed characteristics or developing the relevant chemometric models form their corresponding spectral data, some mathematical pre-processing treatments could be applied to eliminate or minimize such systematic noise from the original spectra and to increase the signal-to-noise ratio. These pretreatment mathematical approaches involve mean centering, auto-scaling, normalization, derivatives, multiplicative scatter correction (MSC), standard normal variate (SNV), and other techniques BIB011 BIB017 . In general, spectral bands can be evaluated explanatively, and if there is a pattern between absorption data and reflectance measurements, the next step is to use a classification or prediction model. From the spatial domain aspects, different image processing routines such as edge detection, filtering, band ratio, thresholding, skeletonization and textural analysis algorithms could be implemented individually or in parallel to extract other extra informative features about seed inherent quality BIB014 . Above all, despite the modeling algorithm used or spectral preprocessing methods applies, it is very important to have appropriate sampling protocols, accurate reference analysis methods, reasonable spectral precision and excellent presentation of the samples to the imaging instrument BIB010 .