Abstract
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.
Highlights
Authentication of powdered food products is becoming increasingly important owing to growing numbers of food fraud incidents, in high value-added products
We investigate the feasibility of non-targeted detection of almond powder adulteration using a one-class classifier analysis method in this study
We reported the feasible detection of adulterated almond powder with apricot and peanuts based on the using Fourier transform near-infrared (FT-NIR) and Fourier transform infrared (FT-IR) spectroscopy for nondestructive authentication of food materials
Summary
Authentication of powdered food products is becoming increasingly important owing to growing numbers of food fraud incidents, in high value-added products. Owing to their multiple unique features and high nutritional value, almonds are one of the most popular nuts consumed worldwide. One of the most notorious food adulteration events was the adulteration of melamine in baby milk, which caused six children to die and several thousand to be hospitalized [4] Another incident by the adulteration of paprika with colored agents caused 60 people to be hospitalized [5]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
