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

Authors: LOHUMI, SANTOSH - Chungnam National University; LEE, HOONSOO - Us Forest Service (FS); Kim, Moon; Qin, Jianwei - Tony Qin; KANDPAL, LALIT - Chungnam National University; BAE, HYUNGJIN - Chungnam National University; RAHMAN, ANISUR - Chungnam National University; CHO, BYOUNG-KWAN - Chungnam National University

Submitted to: PLOS ONE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/27/2018
Publication Date: 4/30/2018
Citation: Lohumi, S., Lee, H., Kim, M.S., Qin, J., Kandpal, L., Bae, H., Rahman, A., Cho, B. 2018. Calibration and testing of a Raman hyperspectral imaging system to reveal powdered food adulteration. PLoS One. 13(4):e0195253. https://doi.org/10.1371/journal.pone.0195253.
DOI: https://doi.org/10.1371/journal.pone.0195253

Interpretive Summary: Increasing concerns over food safety and security in the global marketplace, particularly for powdered foods and ingredients that can be easily mixed with cheap or toxic materials to fraudulently improve perceived measures such as volume, color, or nutrient content, have led to an urgent need to develop rapid, non-destructive, and non-invasive methods to detect food adulterants. Building on previously developed line-scanning Raman hyperspectral imaging (RHSI) technology, this study reports the development of a new RHSI system using customized near-infrared diode-sourced laser-line excitation that reduces mechanical system sensitivities along with improved software design to produce user-friendly real-time detection maps of adulterants in the samples being imaged. System calibration and testing is presented, with wheat flour and paprika powder as food samples adulterated at concentrations ranging from 0.05 to 1%. The adulterants used were benzoyl peroxide (a flour bleaching agent), alloxan monohydrate materials (a byproduct of flour bleaching), and Sudan dye or Congo Red (carcinogenic dyes that can be used for produce paprika-like color). From the detection maps, pixel-based adulterant concentrations were calculated that showed strong correlation to the actual prepared concentrations for the samples (R>0.98). The results show that this new RHSI system may provide a powerful analytical technique for quality and authenticity analysis of food products that can benefit producers and processors seeking to ensure the safety of their products for public consumption.

Technical Abstract: 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.