Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Applications of Multispectral Imaging for Seed Quality Monitoring <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> Applications of Multispectral Imaging for Seed Quality Monitoring <s> In this work, an integrated imaging system to obtain accurate and reproducible multi-spectral dermatological images is proposed. The system is made up of an integrating sphere, light emitting diodes and a generic monochromatic camera. The system can collect up to 10 different spectral bands. These spectral bands vary from ultraviolet to near infrared. The well-defined and diffuse illumination of the optically closed scene aims to avoid shadows and specular reflections. Furthermore, the system has been developed to guarantee the reproducibility of the collected images. This allows for comparative studies of time series of images. Two experiments are conducted to show the ability of the system to acquire highly precise and standardized multi-spectral images. The first experiment aims to show the capacity of the system to collect reproducible images. The second experiment demonstrates that the multi-spectral images provide more information than the classical tri-chromatic images and that this information is enough to segment lesions easily. These two facts together indicate the suitability of the system to collect images and to summarize and track the evolution of dermatological diseases. <s> BIB002 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Applications of Multispectral Imaging for Seed Quality Monitoring <s> The use of camera vision to automatically detect head blight (scab) on wheat ears could provide information about the severity of this dangerous disease and help meet future food traceability requirements. Fusarium spp. is dangerous for both human and animal consumption and the ability to monitor symptom location and severity before the harvested product is further processed or stored could help determine whether the grain is fit for human/animal consumption, for bio-conservation, or is completely unusable. ::: ::: To generate various infection levels, field trials were conducted in 2008 and 2009 using wheat varieties with differing levels of susceptibility to the disease; plots were artificially infected with a spore suspension. A color (red, green, and blue) and a multispectral (red, infrared) camera system with real-time image analysis software were developed and compared to detect disease symptoms in the plots. ::: ::: The chlorophyll defect of the infected wheat ears was classified against the image background by setting binarization thresholds. The result was a black and white image. Single pixels or tiny clusters of pixels not belonging to the symptoms were eliminated by setting an area threshold. For both systems, a linear correlation was found between the camera and the visually detected disease levels of the wheat ears in the plots. ::: ::: In the non-infected control plots without disease symptoms, the multispectral system accurately measured “no disease” even though the digital color system detected too much infection (i.e., a false positive). The multispectral system showed a superior calibration capacity. While the color system had to calibrate for each variety, the multispectral system used only one calibration step before starting the measurements. <s> BIB003 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Applications of Multispectral Imaging for Seed Quality Monitoring <s> Abstract Sensory descriptive analysis was used to evaluate visual and overall freshness and the underlying attributes of freshness of packaged wild rocket ( Diplotaxis tenuifolia L.) quality. Variation was induced by differences in package atmosphere, temperature and storage time. The leaves were packaged in trays wrapped in film with a low oxygen transmission rate (OTR) (low OTR; 0.65 pmol s −1 m −2 kPa −1 ) or high OTR (high OTR; 17.4 pmol s −1 m −2 kPa −1 ). To create packages with variation in O 2 and CO 2 concentrations in the package atmosphere, and in the sensory quality, the packages were stored at 2 °C for 6 or 7 d or at elevated temperatures (10, and 20 °C) for various times (2, 3, 4, and 6 d). At low OTR, the O 2 concentration decreased to ≤0.5 kPa, and the leaves developed a ‘smoked odor’, lost leaf integrity and texture, and turned olive-brown. At high OTR, the O 2 concentrations inside the package remained at 10–18 kPa, and leaves showed signs of senescence, i.e. light-green to yellow leaves and a heterogeneous appearance of leaves in the tray. Freshness of the leaves was evaluated by sensory analysis without prior consensus. ‘Visual freshness’ was evaluated at first sight before opening the packages, while ‘overall freshness’ was evaluated after opening and careful inspection by looking, smelling and touching the product. ‘Visual freshness’ was highly correlated to green leaf color ( r = 0.98), whereas ‘overall freshness’ was described by color, odor and texture as found by interval PLS. The high OTR packages had similar scores for ‘freshness’ before and after opening ( P > 0.05), while the low OTR packages had significantly different scores ( P ≤ 0.05). As appearance is an indication of freshness at purchase, wild rocket must be MAP packaged in an OTR film that permits sufficient levels of O 2 for aerobic respiration when storage temperature cannot be controlled and kept low during distribution and retail. <s> BIB004 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Applications of Multispectral Imaging for Seed Quality Monitoring <s> Abstract Green color and texture are important attributes for the perception of freshness of wild rocket. Packaging of green leafy vegetables can postpone senescence and yellowing, but a drawback is the risk of anaerobic respiration leading to loss of tissue integrity and development of an olive-brown color. The hypothesis underlying this paper is that color and textural quality of packaged wild rocket leaves can be predicted by multispectral imaging for faster evaluation of visual quality of leafy green vegetables in scientific experiments. Multispectral imaging was correlated to sensory evaluation of packaged wild rocket quality. CIELAB values derived from the multispectral images and from a spectrophotometer changed during storage, but the data were insufficient to describe variation in sensory perceived color and texture. CIELAB values from the multispectral images allowed for a more detailed determination of color compared to measurement with a spectrophotometer with an 8 mm aperture. Furthermore, the multispectral images enabled subtraction of background information of the sample and gave more accurate results on visual color of wild rocket leaves. The combination of wavelengths in the NIR range describing textural changes, and wavelengths in the VIS range describing color changes was optimal for quantification of sensory perceived color and textural quality of packaged wild rocket. <s> BIB005 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Applications of Multispectral Imaging for Seed Quality Monitoring <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> BIB006 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Applications of Multispectral Imaging for Seed Quality Monitoring <s> The present study was aimed to investigate the potential of multispectral images coupled with chemometric tools (PLSDA and PLS-R) for: (1.) discriminating different French blue veined cheeses belonging to four brand products (Fourme d’Ambert, Fourme de Montbrison, Bleu d’Auvergne, and Bleu des Causses) and (2.) predicting some of physicochemical (pH, ash, dry matter, total nitrogen, water soluble nitrogen, Ca2+, Na+, Cl−, and P) and rheological properties (softening and dropping points). The results obtained showed that multispectral imaging system applied to anisotropic blue cheeses succeeded to: (1.) discriminate cheeses based on their blue veins features in spite of the visual similarity of their structure and appearance with percentage of correct classification varying between 30 and 100%; and (2.) predict selected parameters (i.e., Ca2+, Cl−, WSN, dropping, and softening points) since R2cv ≥ 0.62 and RPD ≥ 1.62 were obtained. Moreover, the predictive results suggested that the image texture of cheese was strongly related to its physicochemical composition and rheological characteristics (softening and dropping points). <s> BIB007 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Applications of Multispectral Imaging for Seed Quality Monitoring <s> The color assessment ability of a multispectral vision system is investigated by a comparison study with color measurements from a traditional colorimeter. The experiment involves fresh and processed meat samples. Meat is a complex material; heterogeneous with varying scattering and reflectance properties, so several factors can influence the instrumental assessment of meat color. In order to assess whether two methods are equivalent, the variation due to these factors must be taken into account. A statistical analysis was conducted and showed that on a calibration sheet the two instruments are equally capable of measuring color. Moreover the vision system provides a more color rich assessment of fresh meat samples with a glossier surface, than the colorimeter. Careful studies of the different sources of variation enable an assessment of the order of magnitude of the variability between methods accounting for other sources of variation leading to the conclusion that color assessment using a multispectral vision system is superior to traditional colorimeter assessments. <s> BIB008 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Applications of Multispectral Imaging for Seed Quality Monitoring <s> Abstract The aim of this study was to investigate the potential of multispectral imaging supported by multivariate data analysis for the detection of minced beef fraudulently substituted with pork and vice versa. Multispectral images in 18 different wavelengths of 220 meat samples in total from four independent experiments (55 samples per experiment) were acquired for this work. The appropriate amount of beef and pork-minced meat was mixed in order to achieve nine different proportions of adulteration and two categories of pure pork and beef. After an image processing step, data from the first three experiments were used for partial least squares-discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) so as to discriminate among all adulteration classes, as well as among adulterated, pure beef and pure pork samples. Results showed very good discrimination between pure and adulterated samples, for PLS-DA and LDA, yielding 98.48% overall correct classification. Additionally, 98.48% and 96.97% of the samples were classified within a ± 10% category of adulteration for LDA and PLS-DA respectively. Lastly, the models were further validated using the data of the fourth experiment for independent testing, where all pure and adulterated samples were classified correctly in the case of PLS-DA, while LDA was proved to be less accurate. <s> BIB009 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Applications of Multispectral Imaging for Seed Quality Monitoring <s> This paper reviews different image acquisition techniques that have been employed for quality evaluation of leguminous seeds and has relevance for engineers, food scientists and other agricultural researchers. The inspection and quality evaluation of food grains using machine vision can be achieved with greater speed, consistency and accuracy. Image acquisition is central to the success of any quality inspection system based on machine vision. Soybeans, peas, beans, lentils and chickpeas are the legumes, which form the staple food and hence have great ecological and economic importance. The image acquisition techniques that are reviewed in this paper are non-destructive in nature and are based on visible, infrared and other bands of the electromagnetic spectrum. These include techniques for external surface examination, measurement of moisture content, oil content, insect infestation detection and internal structure visualization. The advantages of machine-vision techniques over the conventional techniques based on manual methods for seed quality estimation are also discussed. <s> BIB010 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Applications of Multispectral Imaging for Seed Quality Monitoring <s> There is an increased interest in the applications of hyperspectral imaging (HSI) for assessing food quality, safety, and authenticity. HSI provides abundance of spatial and spectral information from foods by combining both spectroscopy and imaging, resulting in hundreds of contiguous wavebands for each spatial position of food samples, also known as the curse of dimensionality. It is desirable to employ feature selection algorithms for decreasing computation burden and increasing predicting accuracy, which are especially relevant in the development of online applications. Recently, a variety of feature selection algorithms have been proposed that can be categorized into three groups based on the searching strategy namely complete search, heuristic search and random search. This review mainly introduced the fundamental of each algorithm, illustrated its applications in hyperspectral data analysis in the food field, and discussed the advantages and disadvantages of these algorithms. It is hoped that this review should provide a guideline for feature selections and data processing in the future development of hyperspectral imaging technique in foods. <s> BIB011 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Applications of Multispectral Imaging for Seed Quality Monitoring <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> BIB012 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Applications of Multispectral Imaging for Seed Quality Monitoring <s> ABSTRACT The objective of this study was to develop a prototype multispectral imaging system for online quality assessment on pomegranate fruit. At first, a visible/near infrared spectroscopy (400–1100 nm) was tested for non-destructive determination of total soluble solids, titratable acidity, and pH. The spectral data were analyzed using the partial least square analysis. Then to establish consistent multispectral imaging system, the highest absolute values of β-coeffcients correspond to wavelengths from the best partial least square calibration model were selected and used for identifying the optimal wavelengths. Consequently, a multispectral imaging system was developed based on the effective wavelengths 700, 800, 900, and 1000 nm. The performance of the developed multispectral imaging system was evaluated by multiple linear regression models. The multiple linear regression model predict total soluble solids with r = 0.97, root mean square error of calibration = 0.21°Brix, and ratio performance deviation = 6.7 °Brix. Also, the results showed that the models had good predictive ability for pH and titratable acidity. Results showed that the developed multispectral imaging system based on the optimal wavelengths could be used for online quality assessment of pomegranate fruit. <s> BIB013 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Applications of Multispectral Imaging for Seed Quality Monitoring <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> BIB014 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Applications of Multispectral Imaging for Seed Quality Monitoring <s> This study was designed to compare the performances of four different non-destructive methods of assessing onion quality, one of which was based on near-infrared spectroscopy, and three of which were based on spectral imaging. These methods involve a combination of wavelengths from visible to near-infrared with different acquisition systems that were applied to discriminate between pre-sorted onions by in situ measurements of the onion surface. Compared with the partial least squares discriminant analysis classification models associated with different methods, hyperspectral imaging (HSI) with both static horizontal and rotating orientation obtained a higher level of sensitivity and specificity with a lower classification error than did other methods. Moreover, models built with the reduced variables did not lower the model performances. Overall, these results demonstrate that HSI with selected wavelengths would be useful for further developing an improved real-time system for sorting onion bulbs. <s> BIB015 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Applications of Multispectral Imaging for Seed Quality Monitoring <s> BACKGROUND ::: The traditional detection methods for moisture content (MC) and water-holding capacity (WHC) in cooked pork sausages (CPS) are destructive, time consuming, require skilled personnel and are not suitable for online industry applications. The goal of this work was to explore the potential of multispectral imaging (MSI) in combination with multivariate analysis for the identification of MC and WHC in CPS. ::: ::: ::: RESULTS ::: Spectra and textures of 156 CPS treated by six salt concentrations (0-2.5%) were analyzed using different calibration models to find the most optimal results of predicting MC and WHC in CPS. By using the fused data of spectra and textures, partial least squares regression models performed well for determining the MC and WHC, with a correlation coefficient (r) of 0.949 and 0.832, respectively. Additionally, their spatial distribution in CPS could be visualized via applying prediction equations to transfer each pixel in the image. ::: ::: ::: CONCLUSION ::: Results of satisfactory detection and visualization of the MC and WHC showed that MSI has the potential to serve as a rapid and non-destructive method for use in sausage industry. © 2017 Society of Chemical Industry. <s> BIB016 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Applications of Multispectral Imaging for Seed Quality Monitoring <s> Abstract In recent years, fraud detection has become a major priority for food authorities, as fraudulent practices can have various economic and safety consequences. This work explores ways of identifying frozen-then-thawed minced beef labeled as fresh in a rapid, large-scale and cost-effective way. For this reason, freshly-ground beef was purchased from seven separate shops at different times, divided in fifteen portions and placed in Petri dishes. Multi-spectral images and FTIR spectra of the first five were immediately acquired while the remaining were frozen (− 20 °C) and stored for 7 and 32 days (5 samples for each time interval). Samples were thawed and subsequently subjected to similar data acquisition. In total, 105 multispectral images and FTIR spectra were collected which were further analyzed using partial least-squares discriminant analysis and support vector machines. Two meat batches (30 samples) were reserved for independent validation and the remaining five batches were divided in training and test set (75 samples). Results showed 100% overall correct classification for test and external validation MSI data, while FTIR data yielded 93.3 and 96.7% overall correct classification for FTIR test set and external validation set respectively. <s> BIB017
Because the images collected by the multispectral imaging system are in a three-dimensional form, they could be used to analyze minor and subtle variations in the physicochemical properties of tested seeds. This technology has shown promising results in determining different seed quality features. By the way, applying this technology in seed quality evaluation is based basically on the rigorous understanding of the technology fundamentals and how to link seed quality parameters with the information available in the images  . Because the multispectral imaging system has gained widespread acceptance for agro-food, remote sensing, medical and pharmaceutical applications, it has been used in different studies in quality evaluation of different products, for example to predict physicochemical parameters in blue-veined cheeses BIB007 , quality assessment of pomegranate fruit BIB013 , classification of onion bulbs BIB015 , freshness evaluation of rocket BIB004 BIB005 , quality monitoring of pharmaceutical tablets BIB012 , skin characterization and dermatology BIB002 , characterizing sensory properties and physicochemical parameters in meat BIB008 BIB016 , beef microbial safety identification BIB006 and adulteration detection in beef BIB014 BIB017 BIB009 . Although a lot of articles reviewing the potential usages of hyperspectral imaging, RGB imaging, X-ray imaging systems for seed sorting and testing are available  BIB010  , there is only one recent literature review paper prepared by Boelt et al.  just discussing the possibility of using multispectral imaging in detecting fungal infection in barely seeds and maturity development of sugar beet and spinach seeds. Therefore, a much more comprehensive review is needed to highlight recent research endeavors in using fully-operated multispectral imaging systems in seed phenotyping and quality monitoring of different sorts of seeds by providing some examples and research results in characterizing physicochemical quality traits, predicting physiological parameters, detection of defect, pest infestation and seed health. The feature-related wavelength bands can be straightforwardly extracted from the whole spectrum by analyzing hyperspectral imaging data using some well-known wavelength selection algorithms BIB001 BIB011 and these selected wavelengths represent the suitable candidate bands for building an independent multispectral imaging system for the features in concern. In this regard, independent multispectral imaging systems with different designs have been used successfully in different sort of applications in detection and identifying numerous quality features of different seed cultivars. This review article highlights only those research attempts devoted to implementations of fully-functional multispectral imaging systems and does not consider those research attempts that just selected some key wavelengths extracted from hyperspectral data analyses without building fully-functional multispectral imaging systems. Moreover, applications presented in this article only review the research works related to quality evaluation of individual harvested or stored seeds. Although monitoring procedures of seeds during their growth in the open field before harvesting are extremely important because they can provide valuable information about seed overall quality and detailed records about any possible infection or disease invasion, the applications of aerial multispectral imaging systems in the open fields will not be discussed here and should be sought elsewhere (e.g., Dammer et al. BIB003 ). All of these factors make this article the first attempt at summarizing all published multispectral imaging applications in seed phenotyping and quality monitoring as concisely detailed in the following sections and as listed in Table 1 below.