Discriminating Between Apricot Kernels in Ground Almonds: An Inside Look
A recent study demonstrated how near-infrared (NIR) spectroscopy can predict adulteration in apricot kernels with high accuracy.
According to a recent study published in the journal Food Control, near-infrared (NIR) spectroscopy is an effective technique in identifying adulteration of ground almonds (1).
Almonds are one of the most popular nuts globally and contain high nutritional content. As a result, they are a popular food for many consumers, and are often used as a reliable protein source. However, like many foods, almonds are susceptible to food adulteration, which is a growing problem globally. Food production companies and other actors in the food industry have great incentive for altering the ingredients in the food we consume. In many cases, food adulteration results in the use of new chemicals in food that help make the food more addictive and unhealthier.
Wooden spoon with almond meal | Image Credit: © Picture Partners - stock.adobe.com
As a result, detecting adulteration in ground almonds, particularly with apricot kernels, is important. Traditional methods like chromatography, while effective, are costly and destructive (1). Currently, many spectroscopic techniques are being used for this purpose instead. These techniques include hyperspectral imaging (HSI), Raman spectroscopy, nuclear magnetic resonance (NMR) spectroscopy, and fluorescence (FL) spectroscopy (2).
In this study, a multidisciplinary research team comprised of researchers from Agri Ibrahim Cecen University, University of Cordoba, Sivas Science and Technology University, and Canakkale Onsekiz Mart University investigated the potential of using near-infrared (NIR) spectroscopy for identifying almond adulteration. The theory was that NIR spectroscopy, because of its ability to conduct rapid and nondestructive analysis of samples, would be successful in detecting the level of food adulteration in ground almonds (1).
To test their theory, the research team collected 120 almond samples from around Turkey. Then, to gather the NIR spectra, the research team used benchtop and portable spectrometers on each sample (1). Next, the research team had to classify and authenticate ground almonds using machine learning algorithms.
Machine learning (ML) is an important tool in detecting food adulteration. ML algorithms can identify complex patterns in large data sets that are difficult or impossible for humans to detect, offering an automated solution (2). ML can handle and process these large data sets efficiently, providing insights that traditional methods might miss. These ML models can also quickly analyze data and provide accurate results, which is crucial for timely detection of food adulteration (2).
The researchers in this study used soft independent modeling of class analogy (SIMCA) and conditional entropy (CE) with ML algorithms to classify and authenticate ground almonds (1). To predict the apricot kernel levels in adulterated samples, partial least squares regression (PLSR) and CE were used (1).
Between the SIMCA and CE algorithms with the spectral data, the researchers demonstrated the following: These algorithms were able to achieve 100% accuracy in distinguishing between pure and adulterated almond samples (1). The researchers also determined that both portable and benchtop spectrometers performed similarly. They observed high correlation values (rval > 0.96) and a standard error prediction (SEP) of 3.98% for portable units and SEP>4.49 for benchtop units (1).
As a result, the findings showed that SIMCA, PLSR, and CE models can be used in combination with NIR spectroscopy to detect apricot kernel adulteration in ground almonds. The study, therefore, proved the initial theory correct in that NIR spectroscopy, with machine learning algorithms, can be used to detect adulteration in apricot kernels in ground almonds.
References
(1) Menevseoglu, A.; Entrenas, J. A.; Gunes, N.; et al. Machine Learning-assisted Near-Infrared Spectroscopy for Rapid Discrimination of Apricot Kernels in Ground Almond. Food Cont. 2024, 159, 110272. DOI: 10.1016/j.foodcont.2023.110272
(2) Goyal, R.; Singha, P.; Singh, S. K. Spectroscopic Food Adulteration Detection Using Machine Learning: Current Challenges and Future Prospects. Trends Food Sci. Technol. 2024, 146, 104377. DOI: 10.1016/j.tifs.2024.104377
New Study Explores Micro-Raman and FT-IR Analysis of Early Human Tools
June 16th 2025Researchers from Ca’ Foscari University of Venice and the University of Padua have uncovered the earliest direct evidence of Upper Palaeolithic humans deliberately processing the non-edible plant Isatis tinctoria, revealing complex behaviors involving medicinal or dye-related plant use over 32,000 years ago.
Short Tutorial: Complex-Valued Chemometrics for Composition Analysis
June 16th 2025In this tutorial, Thomas G. Mayerhöfer and Jürgen Popp introduce complex-valued chemometrics as a more physically grounded alternative to traditional intensity-based spectroscopy measurement methods. By incorporating both the real and imaginary parts of the complex refractive index of a sample, this approach preserves phase information and improves linearity with sample analyte concentration. The result is more robust and interpretable multivariate models, especially in systems affected by nonlinear effects or strong solvent and analyte interactions.
Bruce R. Kowalski: The Maverick Mind Behind Chemometrics
June 2nd 2025In this Icons of Spectroscopy article, Executive Editor Jerome Workman Jr. delves into the life and impact of Bruce Kowalski, an analytical chemist whose major contributions to chemometrics helped establish the field of applying advanced quantitative and qualitative mathematics to extract meaningful chemical information from complex datasets. Kowalski’s visionary approach to chemical data analysis, education, and software development has transformed the landscape of modern analytical chemistry for academia and industry.
