Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Spectroscopy and Spectral Imaging for Seed Quality Evaluation <s> The oil content of nine different types of oilseeds has been determined by near-infrared reflectance (NIR) spectroscopy. A Northstar computer was used to select the wavelengths that best represent the oil content in these seeds. Selected wavelengths were often in the same area of the spectrum, but calibrations differed with respect to the number of wavelength points required and their order of selection. Wavelength assignments for typical functional groups in fatty acids are discussed. The fatty acid composition and the predominant fatty acid component appeared to influence the wavelengths used for the estimation of oil content in each seed type. The mathematical treatments used appeared to affect absorption maxima of all seed types. Spectra of seed oils and their fatty acids indicated variation and closeness of absorption maxima. <s> BIB001 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Spectroscopy and Spectral Imaging for Seed Quality Evaluation <s> The potential of near-infrared reflectance spectroscopy (NIRS) to detectwithin-plant differences for seed protein content was investigated. Fourhundred and fifty-one single seeds were scanned by NIRS using a specialadapter. After non-destructive NIRS scanning, the seeds were analysed forprotein content by the Dumas combustion method and a calibrationequation was developed. A validation set of 117 additional seeds fromthree individual plants from the cultivars Bristol, Lirajet and Maplus wasanalysed for protein content both by NIRS and combustion. The coefficientof determination between NIRS and combustion values in the validation setwas 0.94, with a standard error of performance (SEP) of 0.77% and aratio of the SEP to the standard deviation (SD) of the validation set of0.28. The coefficient of variation (CV) for seed protein content inindividual plants, as determined by the combustion method, was 11.7%for Bristol, 8.9% for Lirajet, and 9.5% for Maplus. The comparison ofsuch variation with the standard error (SE) of NIRS analysis, defined as thecombination of the SE of the combustion method and the SEP of NIRScalibration equation, revealed that the maximum explainable variance withinindividual plants that can be detected using NIRS analysis of proteincontent in single seeds was 0.86 for Bristol, 0.83 for Lirajet, and 0.85 forMaplus. These results demonstrated that NIRS is a powerful tool fornon-destructive assessment of within-plant variation for seed protein contentin rapeseed. <s> BIB002 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Spectroscopy and Spectral Imaging for Seed Quality Evaluation <s> Abstract Near infrared reflectance spectroscopy (NIRS) was explored as a technique to predict moisture (M), oil and crude protein (CP) content on intact sunflower seeds ( Helianthus annuus L.). Three hundred samples were scanned intact in a monochromator instrument NIRS 6500 (NIRSystems, Silver Spring, MD, USA). Calibration equations were developed using modified partial least square regression (MPLS) with internal cross validation. Samples were split in two sets, one set used as calibration ( n =250) where the remaining samples ( n =50) were used as validation set. Two mathematical treatments (first and second derivative), none (log 1/ R ) and standard normal variate and detrend (SNVD) as scatter corrections were explored. The coefficient of determination in calibration ( R cal 2 ) and the standard error in cross validation (SECV) were 0.95 (SECV: 3.3) for M; 0.96 (SECV: 13.1) for CP and 0.90 (SECV: 22.3) for oil in g kg −1 on a dry weight basis (second derivative, 400–2500 nm). Prediction models accounted for less than 65, 70 and 72% of the total variation for oil, M and CP, respectively. However, it was concluded that NIRS is a suitable technique to be used as a tool for rapid pre-screening of quality characteristics on breeding programs. <s> BIB003 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Spectroscopy and Spectral Imaging for Seed Quality Evaluation <s> The focus of the study is high quality image acquisition in colorimetric and multispectral formats. The aim is to combine the spatial resolution of digital images with the spectral resolution of color measurement instruments, to allow for accurate colorimetric and spectral measurements in each pixel of the acquired images. An experimental image acquisition system is used, which besides trichromatic RGB filters also provides the possibility of acquiring multi-channel images, using a set of narrowband filters. To derive mappings to colorimetric and multispectral representations, two conceptually different approaches are used. In the model-based characterization, the physical model describing the image acquisition process is inverted, to reconstruct spectral reflectance from the recorded device response. In the empirical characterization, the characteristics of the individual components are ignored, and the functions are derived by relating the device response for a set of test colors to the corresponding colorimetric and spectral measurements, using linear and polynomial least squares regression. The results indicate that for trichromatic imaging, accurate colorimetric mappings can be derived by the empirical approach, using polynomial regression to CIEXYZ and CIELAB. However, accurate spectral reconstructions requires for multi-channel imaging, with the best results obtained using the model-based approach. <s> BIB004 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Spectroscopy and Spectral Imaging for Seed Quality Evaluation <s> ABSTRACT Near-infrared reflectance (NIR) spectroscopy can be used for fast and reliable prediction of organic compounds in complex biological samples. We used a recently developed NIR spectroscopy instrument to predict starch, protein, oil, and weight of individual maize (Zea mays) seeds. The starch, protein, and oil calibrations have reliability equal or better to bulk grain NIR analyzers. We also show that the instrument can differentiate quantitative and qualitative seed composition mutants from normal siblings without a specific calibration for the constituent affected. The analyzer does not require a specific kernel orientation to predict composition or to differentiate mutants. The instrument collects a seed weight and a spectrum in 4–6 sec and can collect NIR data alone at a 20-fold faster rate. The spectra are acquired while the kernel falls through a glass tube illuminated with broad spectrum light. These results show significant improvements over prior single-kernel NIR systems, making this inst... <s> BIB005 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Spectroscopy and Spectral Imaging for Seed Quality Evaluation <s> With the development of soybean producing and processing, the quality breeding becomes more and more important for soybean breeders. Traditional sampling detection methods for soybean quality need to destroy the seed, and does not satisfy the requirement of earlier generation materials sieving for breeding. Near infrared (NIR) spectroscopy has been widely used for soybean quality detection. However, all these applications were referred to mass samples, and they were not suitable for little or single seed detection in breeding procedure. In the present study, the acousto--optic tunable filter (AOTF) NIR spectroscopy was used to measure the single soybean seed. Two varieties of soybean were measured, which contained 60 KENJIANDOU43 seeds and 60 ZHONGHUANG13 seeds. The results showed that NIR spectra combined with soft independent modeling of class analogy (SIMCA) could accurately discriminate the soybean varieties. The classification accuracy for KENJIANDOU43 seeds and ZHONGHUANG13 was 100%. The spectra of single soybean seed were measured at different positions, and it showed that the seed shape has significant influence on the measurement of spectra, therefore, the key point for single seed measurement was how to accurately acquire the spectra and keep their representativeness. The spectra for soybeans with glossy surface had high repeatability, while the spectra of seeds with external defects had significant difference for several measurements. For the fast sieving of earlier generation materials in breeding, one could firstly eliminate the seeds with external defects, then apply NIR spectra for internal quality detection, and in this way the influence of seed shape and external defects could be reduced. <s> BIB006 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Spectroscopy and Spectral Imaging for Seed Quality Evaluation <s> Figures 3(a) and 4(a) of this paper published as J. Near Infrared Spectrosc. 19(3), 171–180 (2011) were incorrect. The correct version of these figures is printed below. We apolgise for any confusion.Figure 3.Extended canonical variance component 1 (ECV#1) plot against component 2 of MSC data. (a) Seed with and without pericarp determined as naked and coated and (a+b) the effect of accelerated aging of the seeds.Figure 4.Sample number plot against (a) ECV component 1 of EMSC transformed spectra and (b) ECVA loading plot. Results are from seed lot A with preicarp and germination counted after 21 days. <s> BIB007 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Spectroscopy and Spectral Imaging for Seed Quality Evaluation <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> BIB008 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Spectroscopy and Spectral Imaging for Seed Quality Evaluation <s> Contamination of grain products by fungus can lead to economic losses and is deleterious to human and livestock health. Detection and quantification of fungus-infected corn kernels would be advantageous for producers and breeders in evaluating quality and in selecting hybrids with resistance to infection. This study evaluated the performance of single-kernel near-infrared reflectance spectroscopy (NIRS) and color imaging to discriminate corn kernels infected by eight fungus species at different levels of infection. Discrimination was done according to the level of infection and the mold species. NIR spectra (904 to 1685 nm) and color images were used to develop linear and nonlinear prediction models using linear discriminant analysis (LDA) and multi-layer perceptron (MLP) neural networks. NIRS was able to accurately detect 98% of the uninfected control kernels, compared to about 89% for the color imaging. Results for detecting all levels of infection using NIR were 89% and 79% for the uninfected control and infected kernels, respectively; color imaging was able to discriminate 75% of both the control and infected kernels. In general, there was better discrimination for control kernels than for infected kernels, and certain mold species had better classification accuracy than others when using NIR. The vision system was not able to classify mold species well. The use of principal component analysis on image data did not improve the classification results, while LDA performed almost as well as MLP models. LDA and mean centering NIR spectra gave better classification models. Compared to the results of NIR spectrometry, the classification accuracy of the color imaging system was less attractive, although the instrument has a lower cost and a higher throughput. <s> BIB009 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Spectroscopy and Spectral Imaging for Seed Quality Evaluation <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> BIB010 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Spectroscopy and Spectral Imaging for Seed Quality Evaluation <s> Near Infrared Spectroscopy (NIRS) analysis at the single seed level is a useful tool for breeders, farmers, feeding facilities, and food companies according to current researches. As a non-destructive technique, NIRS allows for the selection and classification of seeds according to specific traits and attributes without alteration of their properties. Critical aspects in using NIRS for single seed analysis such as reference method, sample morphology, and spectrometer suitability are discussed in this review. A summary of current applications of NIRS technologies at single seed level is also presented. <s> BIB011 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Spectroscopy and Spectral Imaging for Seed Quality Evaluation <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> BIB012 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Spectroscopy and Spectral Imaging for Seed Quality Evaluation <s> The combination of near infrared reflectance spectroscopy and chemometrics was evaluated to measure oil in whole corn.The interpretation of the data generated allowed to identify regions in the NIR related to oil.The result of this study showed that NIR spectroscopy can be used to predict oil content in corn. The objective of this study was to evaluate the ability of near infrared reflectance (NIR) spectroscopy to determine oil content in whole corn (Zea mays L.) samples sourced from a breeding program. Kernel samples were analysed in reflectance in the VIS and NIR regions (400-2500nm) at 2nm intervals using a scanning monochromator. Samples were scanned in a circular cell cup and reflectance data were stored as logarithm of the reciprocal reflectance (log1/R). Samples were not rotated when spectra collection was made. The coefficients of determination (R2) and the standard error of cross validation (SECV) obtained for the prediction of oil content in the calibration set were 0.90% and 0.17%, respectively. The residual predictive deviation (RPD=SD/SECV) value obtained was 2.3, indicating that these calibrations can be used for qualitative determination of oil content (e.g. low, medium and high) or preliminary screening in whole corn. <s> BIB013 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Spectroscopy and Spectral Imaging for Seed Quality Evaluation <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> BIB014 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Spectroscopy and Spectral Imaging for Seed Quality Evaluation <s> Seed quality is of great importance in optimizing the cost of crop establishment. Rapid and non-destructive seed quality detection methods must therefore be developed for agriculture and the seed production industry. This review focuses primarily on non-destructive techniques, namely machine vision, spectroscopy, hyperspectral imaging, soft X-ray imaging, thermal imaging and electronic nose techniques, for assessing the quality of agricultural seeds. The fundamentals of these techniques are introduced. Seed quality, including chemical composition, variety identification and classification, insect damage and disease assessment as well as seed viability and germinability of various seeds are discussed. We conclude that non-destructive techniques are accurate detection methods with great potential for seed quality assessment. <s> BIB015 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Spectroscopy and Spectral Imaging for Seed Quality Evaluation <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> BIB016 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Spectroscopy and Spectral Imaging for Seed Quality Evaluation <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> BIB017 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Spectroscopy and Spectral Imaging for Seed Quality Evaluation <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> BIB018 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Spectroscopy and Spectral Imaging for Seed Quality Evaluation <s> Abstract Tobacco seeds are a potential feedstock for biofuels. To insure make full use of tobacco seed biomass, the present study was carried out to estimate seed oil, sugar and crude fiber during seed development through near infrared spectroscopy (NIRS) nondestructive determination. Four pre-processing methods, Savitzky-Goly after standard normal variate (SNV-SG), first Savitzky-Goly derivative after standard normal variate (SNV-SG-1stD), Savitzky-Goly after multiplicative scatter correction (MSC-SG) and first Savitzky-Goly derivative after multiplicative scatter correction (MSC-SG-1stD), were respectively performed to optimize the original spectra before establishment of the calibration models. Then linear partial least squares (PLS) and nonlinear least-squares support vector machine (LS-SVM) methods were utilized to develop the calibration models, in which the LS-SVM models were found to have better performance than PLS models. The best LS-SVM models of oil, sugar and crude fiber were established after pre-processed by MSC-SG-1st D. These results indicated that NIRS was suitable to rapidly and accurately analyze tobacco seed composition. <s> BIB019
Recently, optical sensing techniques, including spectroscopy and imaging systems, have been widely utilized for quality and safety evaluation of seeds. Systems based on spectroscopy provide spectral data either by spectroradiometry or spectrophotometry measurements. In spectrophotometry, the light reflected from a sample is related to the incident light as a measure of the spectral reflectance for the sample. In the case of spectrophotometers, a light source is also required, most commonly tungsten-halogen or xenon lamps BIB004 . In recent years, the near infrared spectroscopy (NIRS) method has demonstrated its ability to carry out simultaneous assessment of different quality traits with accuracy comparable to that of traditional wet chemistry analytical methods BIB011 . Depending on the sensor utilized, the spectrometer devices may cover the ultraviolet (UV) range (200-380 nm), the visible (VIS) range (380-750 nm), the shortwave near infrared (SWNIR) region (750-1700 nm) or the whole near infrared (NIR) range (780-2500 nm). The NIRS technology depends on the absorption of near infrared radiation by major constituents such as protein, lipids, carbohydrates and water resulting in a distinctive spectral pattern corresponding to the energy absorption and molecular vibrations relating to overtones and combination of C-H, N-H and O-H functional groups [27] . Scientific records of NIRS applications in seeds are sparse with much innovative implementation of this technique being in laboratory and industrial applications. Due to their advantages, near infrared spectroscopy (NIRS) techniques have been widely utilized for the quality analysis of crop seeds and kernels including quantitative analyses of oil content BIB001 , protein BIB013 BIB002 , water BIB003 , sugar, starch and carbohydrate content BIB019 BIB005  , viability and vigor detection BIB014 BIB007 BIB008 BIB018 , purity or variety discrimination BIB006 , detection of disease and insect infestation BIB009 , detection of physical damage BIB014 BIB010 and other applications BIB015 . However, the spectral information extracted by this method is presumably limited to only a little portion of a sample where the measuring probe is positioned, without considering the spatial information. Thus, the sample being analyzed should be reasonably homogeneous in order to extract representative spectral fingerprint of the whole sample. This disadvantage of conventional spectroscopy can be easily alleviated by combining both spectral and spatial information by using hyperspectral (HIS) and/or multispectral (MIS) imaging techniques BIB016 . In addition to the spectral information that can be extracted from the images, spectral imaging systems are superior to traditional single-point NIR spectroscopy ones due to their capacity to show the spatial distribution of chemical constituents in the sample in forms called chemical images. Due its ability to combine the merits of spectroscopy and imaging, spectral imaging techniques have been invented to extensively investigate several aspects of seed quality determination BIB012 . Various applications in seed quality assessment using spectral imaging were reported, such as quality grading, disease and infestation detection, variety identification and classification, and prediction of chemical composition  BIB017 .