Screening Method for the Detection of Other Allergenic Nuts in Cashew Nuts Using Chemometrics and a Portable Near-Infrared Spectrophotometer

• Published in: Food analytical methods Vol. 15; no. 4; pp. 1074 - 1084
• Main Authors: Miaw, Carolina Sheng Whei, Martins, Mário Lúcio Campos, Sena, Marcelo Martins, de Souza, Scheilla Vitorino Carvalho
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.04.2022; Springer Nature B.V
• Subjects: Adulterants; algorithms; allergenicity; Anacardiaceae; Analytical Chemistry; Antioxidants; Brazil; cashew nuts; Chemistry; Chemistry and Materials Science; Chemistry/Food Science; chemometrics; Contaminants; Contamination; cross contamination; Discriminant analysis; Food; Food contamination; food fraud; Food Science; Fraud; Genetic algorithms; I.R. radiation; Infrared spectra; Infrared spectrophotometers; Least squares; Macadamia; Macadamia nuts; Microbiology; Minerals; Near infrared radiation; Nuts; Peanuts; Pecan nuts; pecans; Portable equipment; Selectivity; spectrophotometers; toxicity; Vitamins; Brazil; Allergens; Food allergy; Class modelling; Nuts; Handheld NIRS sensor; Vibrational spectroscopy
• ISSN: 1936-9751; 1936-976X
• DOI: 10.1007/s12161-021-02184-0

Nuts and peanuts are foods that are rich in minerals, vitamins, fibre and healthy fats in addition to antioxidant compounds. However, these food products can be subject to adulterations and fraud mainly due to their cost or contamination as a result of improper handling. Different types and degrees of damage can be caused to consumers due to food fraud, highlighting the serious consequences that can occur when the adulterant is toxic or allergenic. In this paper, portable near-infrared (NIR) spectroscopy combined with multivariate supervised classification was proposed to detect peanuts, Brazil nuts, macadamia nuts and pecan nuts in cashew nut samples, covering a wide concentration range (10.0 to 0.1 % w/w) of adulterants/contaminants. Methods to predict five classes of samples, cashew nuts unadulterated and adulterated with peanuts, Brazil nuts, macadamia nuts and pecan nuts, were developed. Three variable selection strategies were tested: interval partial least squares (iPLS), genetic algorithm (GA) and the combination of iPLS-GA. Partial least squares discriminant analysis (PLS-DA) and soft independent modelling of class analogy (SIMCA) models were compared, and PLS-DA coupled with iPLS-GA provided the best results, with sensitivity between 81 and 93 % and selectivity between 94 and 100 %. Applicability for the rapid and non-destructive detection of fraud and cross-contamination with different types of allergenic nuts with portable equipment was demonstrated.