Shortwave Infrared Hyperspectral Imaging System Research Articles

Quantitative detection of benzoyl peroxide in wheat flour using line-scan short-wave infrared hyperspectral imaging

The addition of benzol peroxide (BPO) to wheat flour as a bleaching agent has been widely recognized as an important food safety issue due to its negative effects on human health. To address this issue, various nondestructive optical-based techniques have been developed to screen for BPO, such as Raman spectroscopy and hyperspectral imaging (HSI). In this study, a shortwave infrared (SWIR) HSI system was developed for the rapid detection of BPO particles in wheat flour. The SWIR HSI system, detailed hyperspectral image processing procedures, and optimal model to detect BPO particles were evaluated. The model was developed using the partial least square regression (PLSR) method. To improve performance of the model, effective wavelength regions were selected, and various pre-processing methods were applied to the PLSR analysis. The developed model was able to detect BPO in wheat flour at 50–6400 ppm with a high determinant coefficient (> 0.985) between predicted and actual values. The developed SWIR HIS system and optimized model demonstrated a high potential for discriminating BPO particles in wheat flour and allowed for its quantitative evaluation.

Bulk scanning method of a heavy metal concentration in tailings of a gold mine using SWIR hyperspectral imaging system

This work introduces a bulk data acquisition method using a hyperspectral imaging system (HIS) to measure chromium (Cr) concentration in the soil samples obtained from tailings of a gold mine considering of the spectral competition between heavy metal elements. The chemical, mineralogical, and spectroscopic analyses in a laboratory environment revealed that heavy metal elements' competitive geochemical behaviors were manifested as spectral competitions between heavy metal elements in tailings. The heavy metal elements can be classified into two groups based on their geochemical behaviors: chromium-nickel (Cr-Ni) and zinc-arsenic-cadmium-lead (Zn-As-Cd-Pb). We found an inverse relationship between the two groups in their spectral absorption depth changing patterns, possibly caused by their competition in bonding with the agent minerals. The prediction model of Cr concentrations in the tailing samples using a short-wave infrared (SWIR) HIS was developed from the sample data analysis. The imaging model of Cr concentration is statistically significant with R2 = 0.7 and NRMSE = 11% to 12%. The future use of HIS for massive data acquisition of heavy metal concentration in a natural environment is made possible with such pilot spectroscopic analyses presented in this work.

Shortwave infrared hyperspectral imaging system coupled with multivariable method for TVB-N measurement in pork

Monitoring and maintaining the freshness of meat is important to ensuring a supply of meat that is safe for consumption. The objective of this study is to present a shortwave infrared (SWIR) hyperspectral imaging system in combination with partial least-squares regression (PLSR) model and feature selection methods that can be used for the prediction of the total volatile basic nitrogen (TVB-N) content in fresh pork. The SWIR hyperspectral reflectance images were acquired for pork samples removed from refrigerated storage after 1, 4, 8, 11, 15, and 21 days. The hyperspectral SWIR images and actual TVB-N contents were used for constructing the PLSR model. PLSR models were optimized by using feature selection strategies such as random frog (RF) and variable importance in projection (VIP) score. The predictions from the optimal RF-PLSR model value with maximum normalization preprocessing exhibited correlation coefficient values for Rc2 and Rp2 of 0.94 and 0.90, respectively. Moreover, this research showed that visualization of TVB-N levels applied to the optimal model based on selected wavebands provide an intuitive way to interpret the spatial information of the sample. This study revealed that the multivariate models developed here for rapid and nondestructive evaluation of pork freshness can be feasible for use in online inspection systems as an effective substitute for traditional methods to evaluate pork freshness.

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.

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

ABSTRACTExtensive 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.