Handheld Spectral Sensing Devices Should Not Mislead Consumers as Far as Non-Authentic Food Is Concerned: A Case Study with Adulteration of Milk Powder

Abstract

With the rising trend of consumers being offered by start-up companies portable devices and applications for checking quality of purchased products, it appears of paramount importance to assess the reliability of miniaturized sensors embedded in such devices. Here, eight sensors were assessed for food fraud applications in skimmed milk powder. The performance was evaluated with dry- and wet-blended powders mimicking adulterated materials by addition of either ammonium sulfate, semicarbazide, or cornstarch in the range 0.5–10% of profit. The quality of the spectra was assessed for an adequate identification of the outliers prior to a deep assessment of performance for both non-targeted (soft independent modelling of class analogy, SIMCA) and targeted analyses (partial least square regression with orthogonal signal correction, OPLS). Here, we show that the sensors have generally difficulties in detecting adulterants at ca. 5% supplementation, and often fail in achieving adequate specificity and detection capability. This is a concern as they may mislead future users, particularly consumers, if they are intended to be developed for handheld devices available publicly in smartphone-based applications.

How Chemometrics Can Fight Milk Adulteration

“…One of the most famous extensions of PLS is orthogonal PLS (OPLS) [98], which removes the systematic variation from X that is not correlated (orthogonal) to Y. It was used by Delatour et al [99] on data collected from eight different NIR and MIR miniature sensors to measure the amount of semicarbazide hydrochloride, ammonium sulfate, and cornstarch in skimmed milk powder [96]. Another different use of PLS regression, synergy interval PLS (siPLS) [100], has been used by Vinciguerra et al to quantify cheese whey in cow's milk samples through MIR spectroscopy [38].…”

“…Most of the revised works, especially the ones dealing with infrared data, apply different preprocessing strategies, such as smoothing, standard normal variate (SNV) or multiplicative scatter correction (MSC), and derivatives alone or in combination [87,99,124]. Later on, they select the most appropriate one to solve the specific adulteration issue based on the performance criteria obtained in the developed models.…”