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A recent FAN blog described non-destructive impedance sensors as a tool to classify meat freshness.

In this paper (open access) the authors have used the same principle and developed a classification model for potato varieties based on the effect of their dry matter content on an electrical impedance sensor.  The test is destructive as the potato must be sliced.  The authors built a reference database from data from 9 cultivars (Actrice, Ambra, Constance, El Mundo, Fontane, Gaudi, Jelly, Monalisa and Universa) sourced directly from the grower.  These cultivars were chosen because they cover a wide range of dry matter content.  The authors collected multivariate analytical data from the impedance sensor; impedance magnitude and phase data along with derived parameters such as the minimum phase point of each spectrum, the ratio between the low- and high-frequency values of the impedance magnitude,  the dissipation factor, the distance between the zero and the maximum value of the Nyquist plot, and  the Cole model equivalent circuit parameters.

They conclude that machine learning methods for predicting potato dry matter and varieties, based on impedance data, can achieve an equivalent (sub-optimal) performance to conventional methods and that they hold promise for future improvement to surpass conventional methods. An improved deeper analysis could aim to reduce the root-mean-squared error and increase the coefficient of determination value, thereby enhancing the accuracy of dry matter data predictions. To achieve this, various techniques such as feature engineering, hyperparameter tuning, and advanced modelling approaches (e.g. convolutional neural networks) could be explored. The authors consider that alternate chemometric methods like the Kennard-Stone algorithm, which selects representative samples based on distance criteria, could lead to more robust dataset partitioning. Additionally, incorporating data fusion with results obtained through infrared spectroscopy could further improve the model’s performance.

Photo by Rodrigo dos Reis on Unsplash

There is no single definitive test for dilution of honey with foreign sugar syrups.  An untargeted test, often used to contribute to an analytical weight of evidence, is proton NMR followed by chemometric pattern recognition based on variations in the sugars profile.  One disadvantage of this technique is a lack of sensitivity. 

LCMS is a more sensitive technique and could – in principle – be used in a similar untargeted manner to drive pattern recognition statistics based on the sugar profiles of a database of reference honeys.  The limiting factor has been the computing power that would be needed to “re-set” the database each time a new chromatographic peak is measured or data from different chromatographic systems are combined. (this is why untargeted LCMS is often used in authenticity testing as a 1-off development tool to identify marker compounds, which are then used as the basis for a more routine targeted test, rather than being used as a routine untargeted test).

In this paper (open access), the authors resolved the computing power limitation by using their Bucketing of Untargeted LC-MS Spectra (BOULS) data processing approach which they have previously published.  They demonstrated that untargeted LCMS testing (combining data from different systems, HILIC column with MS in both positive and negative ionisation mode) could discriminate a range of adulterated honeys (rice, beet and high-fructose corn syrups added at 5% to a reference set of 34 North German honeys) from their unadulterated counterparts.

As is the case with all untargeted analytical techniques, the key to using this method routinely would be building a robust reference database of verified authentic honeys that is fully representative of all types and origins on the market.

Photo by Roberta Sorge on Unsplash

We have now added signposts to the free IAEA training and Excel add-in for chemometrics to our permanent resources lists.  You can find details on our e-seminars page.  To aid navigation, it is also listed within mitigation tools.  The add-in is invaluable to any laboratory building an authenticity classification model based upon multivariate analysis of known reference samples.

Spectroscopic methods were used in this study for the discrimination of durum and common wheat samples since they are rapid, reliable, easy to use, low cost, environmentally friendly, and non-destructive. For this purpose, 120 common and 119 durum wheat samples with different genotypes were collected from various regions in Turkey and analysed using Raman spectroscopy, near-infrared spectroscopy (NIR), synchronous fluorescence spectroscopy (SFS), and attenuated total reflectance Fourier-transform infrared spectroscopy (ATR-FTIR). Data analysis was performed using the principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA).

These spectroscopic tools, combined with chemometric analysis, were generally successful in distinguishing common and durum wheat flour samples. It was found that the best method was SFS with a discrimination rate of 100% based on high sensitivity (1.000) and specificity (1.000) values. The effectiveness of the models in which NIR and ATR-FTIR spectroscopies were used was found to be highly similar in terms of the discrimination of durum and common wheat samples. Data obtained from Raman Spectroscopy demonstrated that the method was less sensitive in discriminating between common and durum wheat flour samples than the other spectroscopic techniques with a quite high RMSEP value (0.441). SFS, ATR-FTIR, and NIR spectroscopies proved to be more sensitive and applicable tools than Raman spectroscopy in the discrimination of common and durum wheat samples.

 Read full article.

Food fraud and adulteration is a major concern in terms of economic and public health.Multivariate methods combined with spectroscopic techniques have shown promise as a novel analytical strategy for addressing issues related to food fraud that cannot be solved by the analysis of one variable, particularly in complex matrices such distilled beverages.

This review describes and discusses different aspects of whisky production, and recent developments of laboratory, in field and high throughput analysis. In particular, recent applications detailing the use of vibrational spectroscopy techniques combined with data analytical methods used to not only distinguish between brand and origin of whiskey but to also detect adulteration are presented.

Read open access paper here.