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Injera is a dietary staple in Ethiopia, eaten with most meals.  It is a flatbread made with teff flour.  Injera is vulnerable to adulteration with cheaper gesso or cassava flours.

This paper (purchase required) reports a simple, affordable, portable, and easy-to-use method based on a paper analytical device to indicate adulteration qualitatively.

The authors report that the developed test card generated a red-orange colour on lane B (ferric detection), red on lane D (ferrous detection), Prussian blue on lane F (ferric detection), and Turnbull’s blue colour on lane H (ferrous detection) for pure teff injera. The colour barcodes generated by pure teff injera differ from those produced by teff injera that contain gesso or cassava.

In a survey of local market produce, the test card colour result was less intense or inactive in most cases. It indicates that inexpensive cereals might be used in place of authentic teff flour or flours have been blended before baking.

The authors cross-validated their method by analysing the elemental composition of samples using microwave plasma atomic emission spectrometers.

Photo by Syed F Hashemi on Unsplash

This paper (open access) looked at classifying quinoa, amaranth and wheat flours.

Reference mixtures were prepared in-house:

i) Pure flours, including wheat, quinoa, and amaranth, with two varieties analysed for both quinoa and amaranth;

ii) Double mixtures, which comprised binary combinations of quinoa:wheat flours at 50:50 and 25:75 ratios, and amaranth:wheat flours at 20:80 and 10:90 ratios; and

iii) Triple mixtures, involving combinations of quinoa, amaranth, and wheat flours at 25:10:65 and 12.5:5:82.5 ratios

Volatile profiles of all reference mixtures were measured by both SPME-GC-MS and using a previously-published “electronic nose” sensor (a multiplex of 8 electrochemical sensors).

Twenty-four volatile compounds were identified, including limonene, 1R-α-pinene, and L-β-pinene, which were exclusive to pseudocereal flours, and hexanal, abundant in wheat flour as an oxidation indicator. The authors report that the E-nose achieved 89.7 % accuracy in discriminating between quinoa, amaranth, and wheat flours and effectively separated double and triple mixtures. A PLS model revealed a strong correlation between E-nose data and concentrations of limonene, α-pinene, and β-pinene (R²CV = 0.94–0.95). The integration of GC-MS and E-nose proved highly efficient for flour authentication, with canonical discriminant analysis successfully identifying pseudocereal flours in mixtures with wheat flour,

Photo by Vlad Kutepov on Unsplash

This study (purchase required) evaluated two portable NIR spectrometers (900–1700 nm and 1450–2450 nm) and a benchtop FTIR device (4000–550 cm⁻¹) for authenticating edible insect flours. The reference data were constructed from flours produced in-house from insects or larvae purchased online: mealworm (Tenebrio molitor) larvae (23 samples), buffalo worm (Alphitobius diaperinus) larvae (28 samples) and crickets (Acheta domesticus) (28 samples).  Data-Driven Soft Independent Modelling Class Analogy (DD-SIMCA) and soft Partial Least Squares Discriminant Analysis (sPLS-DA), were used on the spectral data.

Principal Component Analysis (PCA) showed that spectral data of pure insect flours were clustered in the scores plot. DD-SIMCA achieved 100 % sensitivity (SNS) in the test set using FTIR for all insects. NIR Spectrometer in the range of 1450–2450 nm reached 100 % SNS and 100 % specificity (SPS) for buffalo worm and mealworm flour. sPLS-DA showed class sensitivity (CSNS) between 75 % and 100 %, for all three devices tested, with spectrometer in the range of 1450–2450 nm reaching class efficiency rate (CEFF) and total efficiency (TEFF) values ranging from 93 % to 100 %. Also, PLSR achieved RMSEP values as low as 0.44 %, demonstrating its robustness as a tool.

The authors conclude that IR spectroscopy with soft modelling is a non-destructive solution for authenticating insect flours, filling the current gap in rapid and reliable analytical tools for this emerging industry.

Photo by Olga Kudriavtseva on Unsplash

This paper (open access) reports the construction of a classification model to detect the adulteration of white pepper with mung bean flour utilizing Fourier Transform Infrared (FTIR) spectroscopy combined with chemometric techniques.

The authors prepared their own reference samples in-house by grinding locally sourced white pepper (Malaysian origin) with bean flour ranging from 3 – 50%.

They report that adulterants can be detected even at the lowest concentration prepared using the Partial Least Squares (PLS) method and chemometrics.. The second derivative FTIR spectrum in the range of 3712-650 cm⁻¹ was identified as the optimal calibration model.  The PLS Discriminant Analysis (PLS-DA) method also successfully classified pure white pepper samples from those adulterated with various concentrations of mung bean flour.