Near-infrared hyperspectral imaging for online measurement of the viability detection of naturally aged watermelon seeds.

The viability status of seeds before sowing is important to farmers as it allows them to make yield predictions. Monitoring the seed quality in a rapid and nondestructive manner may create a perfect solution, especially for industrial sorting applications. However, current offline laboratory-based strategies employed for the monitoring of seed viability are time-consuming and thus cannot satisfy industrial needs where there is a substantial number of seeds to be analyzed. In this study, we describe a prototype online near-infrared (NIR) hyperspectral imaging system that can be used for the rapid detection of seed viability. A wavelength range of 900-1700 nm was employed to obtain spectral images of three different varieties of naturally aged watermelon seed samples. The partial least square discriminant analysis (PLS-DA) model was employed for real-time viability prediction for seed samples moving through a conveyor unit at a speed of 49 mm/sec. A suction unit was further incorporated to develop the online system and it was programmatically controlled to separate the detected viable seeds from nonviable ones. For an external validation sample set showed classification accuracy levels of 91.8%, 80.7%, and 77.8% in relation to viability for the three varieties of watermelon seed with healthy seedling growth. The regression coefficients of the classification model distinguished some chemical differences in viable and nonviable seed which was verified by the chromatographic analysis after the detection of the proposed online system. The results demonstrated that the developed online system with the viability prediction model has the potential to be used in the seed industry for the quality monitoring of seeds.

Rapid and Non-Destructive Monitoring of Moisture Content in Livestock Feed Using a Global Hyperspectral Model.

The dry matter (DM) content of feed is vital in cattle nutrition and is inversely correlated with moisture content. The established ranges of moisture content serve as a marker for factors such as safe storage limit and DM intake. Rapid changes in moisture content necessitate rapid measurements. A rapid and non-destructive global model for the measurement of moisture content in total mixed ration feed and feed materials was developed. To achieve this, we varied and measured the moisture content in the feed and feed materials using standard methods and captured their images using a hyperspectral imaging (HSI) system in the spectral range of 1000-2500 nm. The spectral data from the samples were extracted and preprocessed using seven techniques and were used to develop a global model using partial least squares regression (PLSR) analysis. The range preprocessing technique had the best prediction accuracy (R2 = 0.98) and standard error of prediction (2.59%). Furthermore, the visual assessment of distribution in moisture content made possible by the generated PLSR-based moisture content mapped images could facilitate precise formulation. These applications of HSI, when used in commercial feed production, could help prevent feed spoilage and resultant health complications as well as underperformance of the animals from improper DM intake.

Development of multi-product calibration models of various root and tuber powders by fourier transform near infra-red (FT-NIR) spectroscopy for the quantification of polysaccharide contents.

The objective of this study was to quantify the chemical content of multiple products using one single calibration model. This study involved seven tuber and root powders from arrowroot, Canna edulis, cassava, taro, as well as purple, yellow, and white sweet potato, for partial least square (PLS) regression to predict polysaccharide contents (i.e., amylose, starch, and cellulose). The developed PLS models showed acceptable results, with Rc 2 of 0.9, 0.95, and 0.85 and SEC of 2.7%, 3.33%, and 3.22%, for amylose, starch, and cellulose, respectively. The models also successfully predicted polysaccharide contents with Rp 2 of 0.89, 0.95, and 0.79; SEP of 2.83%, 3.33%, and 3.55%; and RPD of 3.02, 4.47, and 2.18 for amylose, starch, and cellulose, respectively. These results showed the potential of Fourier transform near-infrared spectroscopy to quantify the chemical composition of multiple products instead of using one individual model.

Hyperspectral Shortwave Infrared Image Analysis for Detection of Adulterants in Almond Powder with One-Class Classification Method.

The widely used techniques for analyzing the quality of powdered food products focus on targeted detection with a low-throughput screening of samples. Owing to potentially significant health threats and large-scale adulterations, food regulatory agencies and industries require rapid and non-destructive analytical techniques for the detection of unexpected compounds present in products. Accordingly, shortwave-infrared hyperspectral imaging (SWIR-HSI) for high throughput authenticity analysis of almond powder was investigated in this study. Two different varieties of almond powder, adulterated with apricot and peanut powder at different concentrations, were imaged using the SWIR-HSI system. A one-class classifier technique, known as data-driven soft independent modeling of class analogy (DD-SIMCA), was used on collected data sets of pure and adulterated samples. A partial least square regression (PLSR) model was further developed to predict adulterant concentrations in almond powder. Classification results from DD-SIMCA yielded 100% sensitivity and 89-100% specificity for different validation sets of adulterated samples. The results obtained from the PLSR analysis yielded a high determination coefficient (R2) and low error values (<1%) for each variety of almond powder adulterated with apricot; however, a relatively higher error rates of 2.5% and 4.4% for the two varieties of almond powder adulterated with peanut powder, which indicates the performance of quantitative analysis model could vary with sample condition, such as variety, originality, etc. PLSR-based concentration mapped images visually characterized the adulterant (apricot) concentration in the almond powder. These results demonstrate that the SWIR-HSI technique combined with the one-class classifier DD-SIMCA can be used effectively for a high-throughput quality screening of almond powder regarding potential adulteration.

Non-Targeted Detection of Adulterants in Almond Powder Using Spectroscopic Techniques Combined with Chemometrics.

Methods that combine targeted techniques and chemometrics for analyzing food authenticity can only facilitate the detection of predefined or known adulterants, while unknown adulterants cannot be detected using such methods. Therefore, the non-targeted detection of adulterants in food products is currently in great demand. In this study, FT-IR and FT-NIR spectroscopic techniques were used in combination with non-targeted chemometric approaches, such as one-class partial least squares (OCPLS) and data-driven soft independent modeling of class analogy (DD-SIMCA), to detect adulterants in almond powder adulterated with apricot and peanut powders. The reflectance spectra of 100 pure almond powder samples from two different varieties (50 each) were collected to develop a calibration model based on each spectroscopic technique; each model was then evaluated for four independent sets of two varieties of almond powder samples adulterated with different concentrations of apricot and peanut powders. Classification using both techniques was highly sensitive, the OCPLS approach yielded 90-100% accuracy in different varieties of samples with both spectroscopic techniques, and the DD-SIMCA approach achieved the highest accuracy of 100% when used in combination with FT-IR in all validation sets. Moreover, DD-SIMCA, combined with FT-NIR, achieved a detection accuracy between 91% and 100% for the different validation sets and the misclassified samples belong to the 5% and 7% adulteration sets. These results suggest that spectroscopic techniques, combined with one-class classifiers, can be used effectively in the high-throughput screening of potential adulterants in almond powder.

Improvement in Purity of Healthy Tomato Seeds Using an Image-Based One-Class Classification Method.

The feasibility of a color machine vision technique with the one-class classification method was investigated for the quality assessment of tomato seeds. The health of seeds is an important quality factor that affects their germination rate, which may be affected by seed contamination. Hence, segregation of healthy seeds from diseased and infected seeds, along with foreign materials and broken seeds, is important to improve the final yield. In this study, a custom-built machine vision system containing a color camera with a white light emitting diode (LED) light source was adopted for image acquisition. The one-class classification method was used to identify healthy seeds after extracting the features of the samples. A significant difference was observed between the features of healthy and infected seeds, and foreign materials, implying a certain threshold. The results indicated that tomato seeds can be classified with an accuracy exceeding 97%. The infected tomato seeds indicated a lower germination rate (<10%) compared to healthy seeds, as confirmed by the organic growing media germination test. Thus, identification through image analysis and rapid measurement were observed as useful in discriminating between the quality of tomato seeds in real time.

Determination of Drying Patterns of Radish Slabs under Different Drying Methods Using Hyperspectral Imaging Coupled with Multivariate Analysis.

Drying kinetics and the moisture distribution map of radish slabs under different drying methods (hot-air drying (HAD), microwave drying (MD), and hot-air and microwave combination drying (HMCD)) were determined and visualized by hyperspectral image (HSI) processing coupled with a partial least square regression (PLSR)-variable importance in projection (VIP) model, respectively. Page model was the most suitable in describing the experimental moisture loss data of radish slabs regardless of the drying method. Dielectric properties (DP, ε ) of radish slices decreased with the decrease in moisture content (MC) during MD, and the penetration depth of microwaves in radish was between 0.81 and 1.15 cm. The PLSR-VIP model developed with 38 optimal variables could result in the high prediction accuracies for both the calibration ( R c a l 2 = 0.967 and RMSEC = 4.32 % ) and validation ( R v a l 2 = 0.962 and RMSEC = 4.45 % ). In visualized drying patterns, the radish slabs dried by HAD had a higher moisture content at the center than at the edges; however, the samples dried by MD contained higher moisture content at the edges. The nearly uniform drying pattern of radish slabs under HMCD was observed in hyperspectral images. Drying uniformity of radish slabs could be improved by the combination drying method, which significantly reduces drying time.

Erratum to: Nondestructive Estimation of Lean Meat Yield of South Korean Pig Carcasses Using Machine Vision Technique.

[This corrects the article DOI: 10.5851/kosfa.2018.e44.].

Improving Sensitivity in Raman Imaging for Thin Layered and Powdered Food Analysis Utilizing a Reflection Mirror.

Raman imaging has been proven to be a powerful analytical technique for the characterization and visualization of chemical components in a range of products, particularly in the food and pharmaceutical industries. The conventional backscattering Raman imaging technique for the spatial analysis of a deep layer suffers from the presence of intense fluorescent and Raman signals originating from the surface layer which mask the weaker subsurface signals. Here, we demonstrated the application of a new reflection amplifying method using a background mirror as a sample holder to increase the Raman signals from a deep layer. The approach is conceptually demonstrated on enhancing the Raman signals from the subsurface layer. Results show that when bilayer samples are scanned on a reflection mirror, the average signals increase 1.62 times for the intense band at 476 cm-1 of starch powder, and average increases of 2.04 times (for the band at 672 cm-1) for a subsurface layer of high Raman sensitive melamine powder under a 1 mm thick teflon sheet. The method was then applied successfully to detect noninvasively the presence of small polystyrene pieces buried under a 2 mm thick layer of food powder (a case of powdered food adulteration) which otherwise are inaccessible to conventional backscattering Raman imaging. In addition, the increase in the Raman signal to noise ratio when measuring samples on a mirror is an important feature in many applications where high-throughput imaging is of interest. This concept is also applicable in an analogous manner to other disciplines, such as pharmaceutical where the Raman signals from deeper zones are typically, substantially diluted due to the interference from the surface layer.

Classification Method for Viability Screening of Naturally Aged Watermelon Seeds Using FT-NIR Spectroscopy.

Viability analysis of stored seeds before sowing has a great importance as plant seeds lose their viability when they exposed to long term storage. In this study, the potential of Fourier transform near infrared spectroscopy (FT-NIR) was investigated to discriminate between viable and non-viable triploid watermelon seeds of three different varieties stored for four years (natural aging) in controlled conditions. Because of the thick seed-coat of triploid watermelon seeds, penetration depth of FT-NIR light source was first confirmed to ensure seed embryo spectra can be collected effectively. The collected spectral data were divided into viable and nonviable groups after the viability being confirmed by conducting a standard germination test. The obtained results showed that the developed partial least discriminant analysis (PLS-DA) model had high classification accuracy where the dataset was made after mixing three different varieties of watermelon seeds. Finally, developed model was evaluated with an external data set (collected at different time) of hundred samples selected randomly from three varieties. The results yield a good classification accuracy for both viable (87.7%) and nonviable seeds (82%), thus the developed model can be considered as a "general model" since it can be applied to three different varieties of seeds and data collected at different time.

Rapid Measurement of Soybean Seed Viability Using Kernel-Based Multispectral Image Analysis.

Viability is an important quality factor influencing seed germination and crop yield. Current seed-viability testing methods rely on conventional manual inspections, which use destructive, labor-intensive and time-consuming measurements. The aim of this study is to distinguish between viable and nonviable soybean seeds, using a near-infrared (NIR) hyperspectral imaging (HSI) technique in a rapid and nondestructive manner. The data extracted from the NIR⁻HSI of viable and nonviable soybean seeds were analyzed using a partial least-squares discrimination analysis (PLS-DA) technique for classifying the viable and nonviable soybean seeds. Variable importance in projection (VIP) was used as a waveband selection method to develop a multispectral imaging model. Initially, the spectral profile of each pixel in the soybean seed images was subjected to PLS-DA analysis, which yielded a reasonable classification accuracy; however, the pixel-based classification method was not successful for high accuracy detection for nonviable seeds. Another viability detection method was then investigated: a kernel image threshold method with an optimum-detection-rate strategy. The kernel-based classification of seeds showed over 95% accuracy even when using only seven optimal wavebands selected through VIP. The results show that the proposed multispectral NIR imaging method is an effective and accurate nondestructive technique for the discrimination of soybean seed viability.

Nondestructive Estimation of Lean Meat Yield of South Korean Pig Carcasses Using Machine Vision Technique.

In this paper, we report the development of a nondestructive prediction model for lean meat percentage (LMP) in Korean pig carcasses and in the major cuts using a machine vision technique. A popular vision system in the meat industry, the VCS2000 was installed in a modern Korean slaughterhouse, and the images of half carcasses were captured using three cameras from 175 selected pork carcasses (86 castrated males and 89 females). The imaged carcasses were divided into calibration (n=135) and validation (n=39) sets and a multilinear regression (MLR) analysis was utilized to develop the prediction equation from the calibration set. The efficiency of the prediction equation was then evaluated by an independent validation set. We found that the prediction equation-developed to estimate LMP in whole carcasses based on six variables-was characterized by a coefficient of determination (Rv 2 ) value of 0.77 (root-mean square error [RMSEV] of 2.12%). In addition, the predicted LMP values for the major cuts: ham, belly, and shoulder exhibited Rv 2 values≥0.8 (0.73 for loin parts) with low RMSEV values. However, lower accuracy (Rv (2) =0.67) was achieved for tenderloin cuts. These results indicate that the LMP in Korean pig carcasses and major cuts can be predicted successfully using the VCS2000-based prediction equation developed here. The ultimate advantages of this technique are compatibility and speed, as the VCS2000 imaging system can be installed in any slaughterhouse with minor modifications to facilitate the on-line and real-time prediction of LMP in pig carcasses.

Through-packaging analysis of butter adulteration using line-scan spatially offset Raman spectroscopy.

Spectroscopic techniques for food quality analysis are limited to surface inspections and are highly affected by the superficial layers (skin or packaging material) of the food samples. The ability of spatially offset Raman spectroscopy (SORS) to obtain chemical information from below the surface of a sample makes it a promising candidate for the non-destructive analysis of the quality of packaged food. In the present study, we developed a line-scan SORS technique for obtaining the Raman spectra of packaged-food samples. This technique was used to quantify butter adulteration with margarine through two different types of packaging. Further, the significant commercial potential of the developed technique was demonstrated by its being able to discriminate between ten commercial varieties of butter and margarine whilst still in their original, unopened packaging. The results revealed that, while conventional backscattering Raman spectroscopy cannot penetrate the packaging, thus preventing its application to the quality analysis of packaged food, SORS analysis yielded excellent qualitative and quantitative analyses of butter samples. The partial least-square regression analysis predictive values for the SORS data exhibit correlation coefficient values of 0.95 and 0.92, associated with the prediction error 3.2 % and 3.9 % for cover-1 & 2, respectively. The developed system utilizes a laser line (ca. 14-cm wide) that enables the simultaneous collection of a large number of spectra from a sample. Thus, by averaging the spectra collected for a given sample, the signal-to-noise ratio of the final spectrum can be enhanced, which will then have a significant effect on the multivariate data analysis methods used for qualitative and/or qualitative analyses. This recently presented line-scan SORS technique could be applied to the development of high-throughput and real-time analysis techniques for determining the quality and authenticity various packaged agricultural products.

Calibration and testing of a Raman hyperspectral imaging system to reveal powdered food adulteration.

The potential adulteration of foodstuffs has led to increasing concern regarding food safety and security, in particular for powdered food products where cheap ground materials or hazardous chemicals can be added to increase the quantity of powder or to obtain the desired aesthetic quality. Due to the resulting potential health threat to consumers, the development of a fast, label-free, and non-invasive technique for the detection of adulteration over a wide range of food products is necessary. We therefore report the development of a rapid Raman hyperspectral imaging technique for the detection of food adulteration and for authenticity analysis. The Raman hyperspectral imaging system comprises of a custom designed laser illumination system, sensing module, and a software interface. Laser illumination system generates a 785 nm laser line of high power, and the Gaussian like intensity distribution of laser beam is shaped by incorporating an engineered diffuser. The sensing module utilize Rayleigh filters, imaging spectrometer, and detector for collection of the Raman scattering signals along the laser line. A custom-built software to acquire Raman hyperspectral images which also facilitate the real time visualization of Raman chemical images of scanned samples. The developed system was employed for the simultaneous detection of Sudan dye and Congo red dye adulteration in paprika powder, and benzoyl peroxide and alloxan monohydrate adulteration in wheat flour at six different concentrations (w/w) from 0.05 to 1%. The collected Raman imaging data of the adulterated samples were analyzed to visualize and detect the adulterant concentrations by generating a binary image for each individual adulterant material. The results obtained based on the Raman chemical images of adulterants showed a strong correlation (R>0.98) between added and pixel based calculated concentration of adulterant materials. This developed Raman imaging system thus, can be considered as a powerful analytical technique for the quality and authenticity analysis of food products.

Detection of melamine in milk powder using MCT-based short-wave infrared hyperspectral imaging system.

Extensive research has been conducted on non-destructive and rapid detection of melamine in powdered foods in the last decade. While Raman and near-infrared hyperspectral imaging techniques have been successful in terms of non-destructive and rapid measurement, they have limitations with respect to measurement time and detection capability, respectively. Therefore, the objective of this study was to develop a mercury cadmium telluride (MCT)-based short-wave infrared (SWIR) hyperspectral imaging system and algorithm to detect melamine quantitatively in milk powder. The SWIR hyperspectral imaging system consisted of a custom-designed illumination system, a SWIR hyperspectral camera, a data acquisition module and a sample transfer table. SWIR hyperspectral images were obtained for melamine-milk samples with different melamine concentrations, pure melamine and pure milk powder. Analysis of variance and the partial least squares regression method over the 1000-2500 nm wavelength region were used to develop an optimal model for detection. The results showed that a melamine concentration as low as 50 ppm in melamine-milk powder samples could be detected. Thus, the MCT-based SWIR hyperspectral imaging system has the potential for quantitative and qualitative detection of adulterants in powder samples.

Non-destructive technique for determining the viability of soybean (Glycine max) seeds using FT-NIR spectroscopy.

BACKGROUND: The viability of seeds is important for determining their quality. A high-quality seed is one that has a high capability of germination that is necessary to ensure high productivity. Hence, developing technology for the detection of seed viability is a high priority in agriculture. Fourier transform near-infrared (FT-NIR) spectroscopy is one of the most popular devices among other vibrational spectroscopies. This study aims to use FT-NIR spectroscopy to determine the viability of soybean seeds. RESULTS: Viable and artificial ageing seeds as non-viable soybeans were used in this research. The FT-NIR spectra of soybean seeds were collected and analysed using a partial least-squares discriminant analysis (PLS-DA) to classify viable and non-viable soybean seeds. Moreover, the variable importance in projection (VIP) method for variable selection combined with the PLS-DA was employed. The most effective wavelengths were selected by the VIP method, which selected 146 optimal variables from the full set of 1557 variables. CONCLUSIONS: The results demonstrated that the FT-NIR spectral analysis with the PLS-DA method that uses all variables or the selected variables showed good performance based on the high value of prediction accuracy for soybean viability with an accuracy close to 100%. Hence, FT-NIR techniques with a chemometric analysis have the potential for rapidly measuring soybean seed viability. © 2017 Society of Chemical Industry.

Quantitative analysis of Sudan dye adulteration in paprika powder using FTIR spectroscopy.

As adulteration of foodstuffs with Sudan dye, especially paprika- and chilli-containing products, has been reported with some frequency, this issue has become one focal point for addressing food safety. FTIR spectroscopy has been used extensively as an analytical method for quality control and safety determination for food products. Thus, the use of FTIR spectroscopy for rapid determination of Sudan dye in paprika powder was investigated in this study. A net analyte signal (NAS)-based methodology, named HLA/GO (hybrid linear analysis in the literature), was applied to FTIR spectral data to predict Sudan dye concentration. The calibration and validation sets were designed to evaluate the performance of the multivariate method. The obtained results had a high determination coefficient (R2) of 0.98 and low root mean square error (RMSE) of 0.026% for the calibration set, and an R2 of 0.97 and RMSE of 0.05% for the validation set. The model was further validated using a second validation set and through the figures of merit, such as sensitivity, selectivity, and limits of detection and quantification. The proposed technique of FTIR combined with HLA/GO is rapid, simple and low cost, making this approach advantageous when compared with the main alternative methods based on liquid chromatography (LC) techniques.

Detection of starch adulteration in onion powder by FT-NIR and FT-IR spectroscopy.

Adulteration of onion powder with cornstarch was identified by Fourier transform near-infrared (FT-NIR) and Fourier transform infrared (FT-IR) spectroscopy. The reflectance spectra of 180 pure and adulterated samples (1-35 wt % starch) were collected and preprocessed to generate calibration and prediction sets. A multivariate calibration model of partial least-squares regression (PLSR) was executed on the pretreated spectra to predict the presence of starch. The PLSR model predicted adulteration with an R(p)2 of 0.98 and a standard error of prediction (SEP) of 1.18% for the FT-NIR data and an R(p)2 of 0.90 and SEP of 3.12% for the FT-IR data. Thus, the FT-NIR data were of greater predictive value than the FT-IR data. Principal component analysis on the preprocessed data identified the onion powder in terms of added starch. The first three principal component loadings and ß coefficients of the PLSR model revealed starch-related absorption. These methods can be applied to rapidly detect adulteration in other spices.