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In pharmaceutical manufacturing, quality monitoring of end products is essential to gain regulatory approval. In particular, monitoring the quantity of an active pharmaceutical ingredient (API) in the administered dosage is key to ensuring the content uniformity of the product. Thus, we herein aim to demonstrate the ability of the newly developed line-scan Raman hyperspectral imaging (RHSI) technique for the quantitative analysis of APIs in microtablet samples. Microtablets containing the API of interest and appropriate excipients of varying concentrations (i.e., 60–130% (w/w) API) were prepared by direct compression. The microtablet RHSI spectra were obtained over a wavelength range of 400–1800 cm −1 . High-performance liquid chromatography was also employed as a reference method for the API assay. Multivariate analysis methods, including partial least squares and least-squares support vector machines, were employed to predict the API concentrations using the spectral and reference values of the microtablets. The developed models exhibited excellent prediction abilities for the API concentration, with a coefficient of correlation (R 2 ) >0.95, which was associated with an error of <5% (w/w) API. Furthermore, visualization of the API concentrations and distributions in the microtablets was achieved through chemical imaging. These results confirmed that line-scan RHSI is a powerful tool for the characterization of pharmaceutical products. In addition, this approach is suitable for application in the pharmaceutical production line for the online inspection of bulk products and would be expected to easily replace conventional measurement techniques. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Determination of the content uniformity, assessed by the amount of an active pharmaceutical ingredient (API), and hardness of pharmaceutical materials is important for achieving a high-quality formulation and to ensure the intended therapeutic effects of the end-product. In this work, Fourier transform near infrared (FT-NIR) spectroscopy was used to determine the content uniformity and hardness of a pharmaceutical mini-tablet and standard tablet samples. Tablet samples were scanned using an FT-NIR instrument and tablet spectra were collected at wavelengths of 1000–2500 nm. Furthermore, multivariate analysis was applied to extract the relationship between the FT-NIR spectra and the measured parameters. The results of FT-NIR spectroscopy for API and hardness prediction were as precise as the reference high-performance liquid chromatography and mechanical hardness tests. For the prediction of mini-tablet API content, the highest coefficient of determination for the prediction ( R 2 p ) was found to be 0.99 with a standard error of prediction (SEP) of 0.72 mg. Moreover, the standard tablet hardness measurement had a R 2 p value of 0.91 with an SEP of 0.25 kg. These results suggest that FT-NIR spectroscopy is an alternative and accurate nondestructive measurement tool for the detection of the chemical and physical properties of pharmaceutical samples. [ABSTRACT FROM AUTHOR]

Monitoring the amount of active pharmaceutical ingredient (API) in finished dosage form is important to ensure the content uniformity of the product. In this report, we summarize the development and validation of a hyperspectral imaging (HSI) technique for rapid in-line prediction of the active pharmaceutical ingredient (API) in microtablets with concentrations varying from 60 to 130% API (w/w). The tablet spectra of different API concentrations were collected in-line using an HSI system within the visible/near-infrared (vis/ NIR; 400-1000 nm) and short-wave infrared (SW1R; 1100-2500 nm) regions. The ability of the HSI technique to predict the API concentration in the tablet samples was validated against a reference high-performance liquid chromatography (HPLC) method. The prediction efficiency of two different types of multivariate data modeling methods, that is, partial least-squares regression (PLSR) and principle component regression (PCR), were compared. The prediction ability of the regression models was cross-validated against results generated with the reference HPLC method. The results obtained from the PLSR models showed reliable performance for predicting the API concentration in SWIR region. The highest coefficient of determination (R[sup 2]p) was 0.96, associated with the lowest predicted error and bias of 4.45 and -0.35%, respectively. Furthermore, the concentration-mapped images of PLSR and PCR models were used to visually characterize the API concentration present on the tablet surface. Based on these results, we suggest that HSI measurement combined with multivariate data analysis and chemical imaging could be implemented in the production environment for rapid in-line determination of pharmaceutical product quality. [ABSTRACT FROM AUTHOR]

• Multisensor data-fusion approach was proposed for fusion of NIR and MIR datasets of tuber flours samples. • Data-fusion model named SOPLS showed significant improvement in the prediction accuracy as compared to using a single sensor model. • Proposed methodology to be applicable for improving consistency, robustness and precision while reducing uncertainty in the prediction model. This study evaluates the near-infrared spectroscopy (NIR) and mid-infrared spectroscopy (MIR) complementary spectral ranges to predict six different quality traits, which include chemical components such as amylose, starch, protein, glucose, cellulose, and moisture contents, in tubers and root flours. The sequential orthogonalized partial least square regression (SOPLS), a recently developed multi-sensor data-fusion approach, was adapted to improve the performance of the model in predicting the chemical properties of the flour samples. Furthermore, the performance of the SOPLS model was compared to that of traditional PLS modeling. Compared to the earlier results acquired using individual sensor modeling (with the traditional PLS model), the SOPLS fusion model showed significant improvement in the prediction performance for all cases except glucose. Particularly, the highest improvement in performance was observed for the prediction of cellulose, showing a 22.8% increase in coefficient of determination for prediction ( R 2 p) and 66.5% decrease in root mean square of prediction (RMSEP) values. Therefore, we concluded that the data-fusion approach used in this study exhibited better performance compared to the model using individual sensors. Furthermore, the multi-sensor fusion with the sequential approach is not limited to NIR and MIR data only and can be used for complementary information fusion to further improve the performance of the model. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Corn is one of the most cultivated crops all over world as food for humans as well as animals. Optimized agronomic practices and improved technological interventions during planting, harvesting and post-harvest handling are critical to improving the quantity and quality of corn production. Seed germination and vigor are the primary determinants of high yield notwithstanding any other factors that may play during the growth period. Seed viability may be lost during storage due to unfavorable conditions e.g. moisture content and temperatures, or physical damage during mechanical processing e.g. shelling, or over heating during drying. It is therefore vital for seed companies and farmers to test and ascertain seed viability to avoid losses of any kind. This study aimed at investigating the possibility of using hyperspectral imaging (HSI) technique to discriminate viable and nonviable corn seeds. A group of corn samples were heat treated by using microwave process while a group of seeds were kept as control group (untreated). The hyperspectral images of corn seeds of both groups were captured between 400 and 2500 nm wave range. Partial least squares discriminant analysis (PLS-DA) was built for the classification of aged (heat treated) and normal (untreated) corn seeds. The model showed highest classification accuracy of 97.6% (calibration) and 95.6% (prediction) in the SWIR region of the HSI. Furthermore, the PLS-DA and binary images were capable to provide the visual information of treated and untreated corn seeds. The overall results suggest that HSI technique is accurate for classification of viable and non-viable seeds with non-destructive manner. [ABSTRACT FROM AUTHOR]

Aflatoxins are toxic metabolites produced by the fungi Aspergillus flavus and Aspergillus parasiticus . They can contaminate a wide range of crops before harvest and during storage. Contaminated grains are associated with economic losses for cultivators as well as potential health hazards to both humans and animals. In this study, a short-wave infrared (SWIR) hyperspectral imaging technique was utilized to detect aflatoxin contamination on corn kernels. Corn samples were inoculated with four different aflatoxin B 1 (AFB1) concentrations (10, 100, 500 and 1000 μg/kg) while control samples were surface-disinfected with a PBS solution. Both infected and control samples were scanned with an SWIR hyperspectral system over the spectral range of 1100–1700 nm. A partial least squares discriminant analysis (PLS-DA) model was developed to categorize control and infected kernels and the highest overall classification accuracy yielded from the developed model was 96.9%. Spectral deviation was observed between the control and inoculated samples as the AFB1 concentrations increased. In addition, the contamination map generated with the PLS-DA model provided the visual appearance of infected samples. Our results suggest that SWIR hyperspectral imaging is a rapid, accurate, and non-destructive technique for the detection of toxic metabolites in grains and could be an alternative to manual techniques. [ABSTRACT FROM AUTHOR]

Spectroscopy has proven to be an efficient tool for measuring the properties of meat. In this article, hyperspectral imaging (HSI) techniques are used to determine the moisture content in cooked chicken breast over the VIS/NIR (400-1,000 nm) spectral range. Moisture measurements were performed using an oven drying method. A partial least squares regression (PLSR) model was developed to extract a relationship between the HSI spectra and the moisture content. In the full wavelength range, the PLSR model possessed a maximum R2p of 0.90 and an SEP of 0.74%. For the NIR range, the PLSR model yielded an R2p of 0.94 and an SEP of 0.71%. The majority of the absorption peaks occurred around 760 and 970 nm, representing the water content in the samples. Finally, PLSR images were constructed to visualize the dehydration and water distribution within different sample regions. The high correlation coefficient and low prediction error from the PLSR analysis validates that HSI is an effective tool for visualizing the chemical properties of meat. [ABSTRACT FROM AUTHOR]

The grading of ginseng (Panax ginseng) including the evaluation of internal quality attributes is essential in the ginseng industry for quality control. Assessment for inner whitening, a major internal disorder, must be conducted when identifying high quality ginseng. Conventional methods for detecting inner whitening in ginseng root samples use manual inspection, which is time-consuming and inaccurate. This study develops an internal quality measurement technique using near-infrared transmittance spectral imaging to evaluate inner whitening in ginseng samples. Principle component analysis (PCA) was used on ginseng hypercube data to evaluate the developed technique. The transmittance spectra and spectral images of ginseng samples exhibiting inner whitening showed weak intensity characteristics compared to normal ginseng in the region of 900–1050 nm and 1150–1400 nm respectively, owing to the presence of whitish internal tissues that have higher optical density. On the basis of the multivariate analysis method, even a simple waveband ratio image has the great potential to quickly detect inner whitening in ginseng samples, since these ratio images show a significant difference between whitened and non-whitened regions. Therefore, it is possible to develop an efficient and rapid spectral imaging system for the real-time detection of inner whitening in ginseng using minimal spectral wavebands. This novel strategy for the rapid, cost-effective, non-destructive detection of ginseng's inner quality can be a key component for the automation of ginseng grading. [ABSTRACT FROM AUTHOR]

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 anR2of 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]