“…Other researchers have used hyperspectral imaging to detect adulteration of chocolate powder with non-cocoa products. 11 Furthermore, NIR spectroscopy may be able to simultaneously profile the fatty acid composition 28,32 or sucrose content 18 of the chocolate layer, allowing identification of products made from lower quality chocolate.…”
Section: Resultsmentioning
confidence: 99%
“…10 However, the penetration depth may be different for the chocolate matrix. 11 As chocolate coatings are usually 1- to 2-mm thick, the NIR signal may include some information relating to the composition of the chocolate-coated centre. Notably, NIR spectroscopy has previously been applied to the detection of internal defects in intact macadamia kernels, 12,13 demonstrating that this technique can be used to detect the internal nut composition through the kernel thickness.Figure 1.The appearance of chocolate-coated peanuts and sultanas.…”
Non-destructively identifying the centre composition of panned chocolate goods may be useful in quality assurance settings. However, no studies to date have investigated this topic. In this study, nearinfrared spectra (1000–2500 nm) were collected from chocolate-coated peanuts and chocolate-coated sultanas ( n = 170 of each) in order to investigate the prospect of non-invasively detecting the composition of the centre. Principal component analysis confirmed that the spectra of these samples were distinct from one another. The partial least squares discriminant analysis (PLS-DA) model showed a high level of separation between chocolate-coated peanuts and sultanas in the training set (R2 = 0.95; RPD = 4.4). Discrimination between peanut and sultana samples from an independent test set was also possible, although with slightly less distinct separation between the sample types. A soft independent modelling by class analogy model was also able to differentiate between the two sample types, albeit with higher levels of misclassification compared to PLS-DA. Incorporating samples from different manufacturers may be useful for improving the broader applicability of the model.
“…Other researchers have used hyperspectral imaging to detect adulteration of chocolate powder with non-cocoa products. 11 Furthermore, NIR spectroscopy may be able to simultaneously profile the fatty acid composition 28,32 or sucrose content 18 of the chocolate layer, allowing identification of products made from lower quality chocolate.…”
Section: Resultsmentioning
confidence: 99%
“…10 However, the penetration depth may be different for the chocolate matrix. 11 As chocolate coatings are usually 1- to 2-mm thick, the NIR signal may include some information relating to the composition of the chocolate-coated centre. Notably, NIR spectroscopy has previously been applied to the detection of internal defects in intact macadamia kernels, 12,13 demonstrating that this technique can be used to detect the internal nut composition through the kernel thickness.Figure 1.The appearance of chocolate-coated peanuts and sultanas.…”
Non-destructively identifying the centre composition of panned chocolate goods may be useful in quality assurance settings. However, no studies to date have investigated this topic. In this study, nearinfrared spectra (1000–2500 nm) were collected from chocolate-coated peanuts and chocolate-coated sultanas ( n = 170 of each) in order to investigate the prospect of non-invasively detecting the composition of the centre. Principal component analysis confirmed that the spectra of these samples were distinct from one another. The partial least squares discriminant analysis (PLS-DA) model showed a high level of separation between chocolate-coated peanuts and sultanas in the training set (R2 = 0.95; RPD = 4.4). Discrimination between peanut and sultana samples from an independent test set was also possible, although with slightly less distinct separation between the sample types. A soft independent modelling by class analogy model was also able to differentiate between the two sample types, albeit with higher levels of misclassification compared to PLS-DA. Incorporating samples from different manufacturers may be useful for improving the broader applicability of the model.
“…Additionally, peanut flour adulteration of cocoa powder is an alarming health concern for consumers with peanut allergies. A study involving training model at proportions of 0%, 0.1%, 1%, 10% and 100% for the determination of peanut flour in cocoa powder was demonstrated recently by pairing NIR spectral data with principal component analysis and multivariate curve resolution-alternating least squares chemometrics [97]. The authors propose the use of their method for the identification and quantification of adulterants in other powders in the future.…”
Quality checks, assessments, and the assurance of food products, raw materials, and food ingredients is critically important to ensure the safeguard of foods of high quality for safety and public health. Nevertheless, quality checks, assessments, and the assurance of food products along distribution and supply chains is impacted by various challenges. For instance, the development of portable, sensitive, low-cost, and robust instrumentation that is capable of real-time, accurate, and sensitive analysis, quality checks, assessments, and the assurance of food products in the field and/or in the production line in a food manufacturing industry is a major technological and analytical challenge. Other significant challenges include analytical method development, method validation strategies, and the non-availability of reference materials and/or standards for emerging food contaminants. The simplicity, portability, non-invasive, non-destructive properties, and low-cost of NIR spectrometers, make them appealing and desirable instruments of choice for rapid quality checks, assessments and assurances of food products, raw materials, and ingredients. This review article surveys literature and examines current challenges and breakthroughs in quality checks and the assessment of a variety of food products, raw materials, and ingredients. Specifically, recent technological innovations and notable advances in quartz crystal microbalances (QCM), electroanalytical techniques, and near infrared (NIR) spectroscopic instrument development in the quality assessment of selected food products, and the analysis of food raw materials and ingredients for foodborne pathogen detection between January 2019 and July 2020 are highlighted. In addition, chemometric approaches and multivariate analyses of spectral data for NIR instrumental calibration and sample analyses for quality assessments and assurances of selected food products and electrochemical methods for foodborne pathogen detection are discussed. Moreover, this review provides insight into the future trajectory of innovative technological developments in QCM, electroanalytical techniques, NIR spectroscopy, and multivariate analyses relating to general applications for the quality assessment of food products.
“…The UV-vis spectra were evaluated by Multivariate Curve Resolution-Alternating Least Squares regression (MCR-ALS) chemometric approach, whose goal is to decompose the collected data into their pure chemical components, by providing their spectra and their concentration profiles (Tauler, 1995;de Juan et al, 2000;Jaumot et al, 2005Jaumot et al, , 2015Ruckebusch and Blanchet, 2013). The MCR-ALS approach has been deeply used in several applications such as, for instance, voltammetry (Serrano et al, 2020), UV-vis (Veselinović et al, 2012;Ghigo et al, 2018), IR (Shariati-Rad and Hasani, 2009), Raman (Andrew and Hancewicz, 1998;Lyndgaard et al, 2013), hyperspectral imaging (Piqueras et al, 2011;Laborde et al, 2021), NMR (Huo et al, 2004), EPR (Abou Fadel et al, 2014;Berto et al, 2019), GC-MS (Lebanov et al, 2020), UHPLC (Wehrens, 2011), etc. It is particularly helpful in case of measurements following Lambert-Beer's law, i.e., in case the collected overall spectra consist in a linear combination of the spectra of their pure components.…”
Section: Multivariate Curve Resolution-alternating Least Squares Regrmentioning
Chemometric techniques were applied to the study of the interaction of iron(III) and tannic acid (TA). Modeling the interaction of Fe(III)–TA is a challenge, as can be the modeling of the metal complexation upon natural macromolecules without a well-defined molecular structure. The chemical formula for commercial TA is often given as C76H52O46, but in fact, it is a mixture of polygalloyl glucoses or polygalloyl quinic acid esters with the number of galloyl moieties per molecule ranging from 2 up to 12. Therefore, the data treatment cannot be based on just the stoichiometric approach. In this work, the redox behavior and the coordination capability of the TA toward Fe(III) were studied by UV-vis spectrophotometry and fluorescence spectroscopy. Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) and Parallel Factor Analysis (PARAFAC) were used for the data treatment, respectively. The pH range in which there is the redox stability of the system Fe(III)–TA was evaluated. The binding capability of TA toward Fe(III), the spectral features of coordination compounds, and the concentration profiles of the species in solution as a function of pH were defined. Moreover, the stability of the interaction between TA and Fe(III) was interpreted through the chemical models usually employed to depict the interaction of metal cations with humic substances and quantified using the concentration profiles estimated by MCR-ALS.
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