Machine Learning-Based Analytical Systems: Food Forensics

Abstract

Despite a large amount of money being spent on both food analyses and control measures, various food-borne illnesses associated with pathogens, toxins, pesticides, adulterants, colorants, and other contaminants pose a serious threat to human health, and thus food safety draws considerable attention in the modern pace of the world. The presence of various biogenic amines in processed food have been frequently considered as the primary quality parameter in order to check food freshness and spoilage of protein-rich food. Various conventional detection methods for detecting hazardous analytes including microscopy, nucleic acid, and immunoassay-based techniques have been employed; however, recently, array-based sensing strategies are becoming popular for the development of a highly accurate and precise analytical method. Array-based sensing is majorly facilitated by the advancements in multivariate analytical techniques as well as machine learning-based approaches. These techniques allow one to solve the typical problem associated with the interpretation of the complex response patterns generated in array-based strategies. Consequently, the machine learning-based neural networks enable the fast, robust, and accurate detection of analytes using sensor arrays. Thus, for commercial applications, most of the focus has shifted toward the development of analytical methods based on electrical and chemical sensor arrays. Therefore, herein, we briefly highlight and review the recently reported array-based sensor systems supported by machine learning and multivariate analytics to monitor food safety and quality in the field of food forensics.

An Encyclopedic Compendium on Chemosensing Supramolecular Metal‐Organic Gels

“…[281] One potential avenue for future exploration is the use of machine learning algorithms to forecast potential applications of soft materials in fields such as soft robotics and smart sensing materials. [282,283] Investigating the initial aggregation of an LMWG is also crucial for a self-assembled fibrillar network (SAFIN) understanding. [284] Lack of understanding of dynamics governing the gelation process, offering a foundational basis for informed material design.…”

“…Furthermore, the majority of discoveries in the field are often serendipitous due to the absence of established theories and techniques for accurately predicting the behavior of noncovalent interactions in solvents with varying degrees of polarity [281] . One potential avenue for future exploration is the use of machine learning algorithms to forecast potential applications of soft materials in fields such as soft robotics and smart sensing materials [282,283] . Investigating the initial aggregation of an LMWG is also crucial for a self‐assembled fibrillar network (SAFIN) understanding [284] .…”

Colorimetric Nanozyme Sensor Array Based on Metal Nanoparticle-Decorated CNTs for Quantification of Pesticides in Real Water and Soil Samples

“…Although frequently employed conventional “lock-and-key” analysis techniques possess excellent sensitivity, they require specific receptors that have a significant affinity to recognize a particular pesticide, leading to costly and lengthy procedures due to the need for numerous antibodies for multipesticide assays. , Additionally, specialized equipment was required for numerous chromatography–mass spectrometry methods as well as flow injection analysis. , Compared with the above conventional methods, array-based pattern recognition has shown great interest in detection of multiple analytes in the sensing field, which places more emphasis on group discrimination than single analyte detection. − Unlike the lock-and-key sensing mode that relies on individual receptors, this strategy utilizes artificial arrays of cross-reactive sensor elements to generate discrete patterns specific to each analyte. The examination of the patterns acquired via the use of multivariate algorithms in machine-learning approaches unveils the specific identification and concentration of the substance being analyzed, enabling the concurrent detection of several substances. − Array-based sensing has been extensively utilized for quantification and the analysis of several toxic substances such as heavy metal ions, thiols, bacteria, biogenic amines, toxic gases, etc.…”