Screening for pesticide residues in cocoa (Theobroma cacao L.) by portable infrared spectroscopy

• Published in: Talanta (Oxford) Vol. 257; p. 124386
• Main Authors: Villanueva, Eudes, Glorio-Paulet, Patricia, Giusti, M. Monica, Sigurdson, Gregory T., Yao, Siyu, Rodríguez-Saona, Luis E.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 15.05.2023
• Subjects: algorithms; butter; Cacao - chemistry; Chocolate - analysis; class; cocoa beans; Cocoa nibs; Field portable; gas chromatography; Gas Chromatography-Mass Spectrometry - methods; Herbicides; infrared spectroscopy; lipids; liquid chromatography; mass spectrometry; Mid-infrared; Organic production; Peru; Pesticide Residues - analysis; pesticides; prediction; rapid methods; reflectance; regression analysis; Spectrophotometry, Infrared; Theobroma cacao; Peru; Herbicides; Mid-infrared; Cocoa nibs; Field portable; Organic production
• ISSN: 0039-9140; 1873-3573; 1873-3573
• DOI: 10.1016/j.talanta.2023.124386

Rapid assessment of pesticide residues ensures cocoa bean quality and marketability. In this study, a portable FTIR instrument equipped with a triple reflection attenuated total reflectance (ATR) accessory was used to screen cocoa beans for pesticide residues. Cocoa beans (n = 75) were obtained from major cocoa growing regions of Peru and were quantified for pesticides by gas chromatography (GC) or liquid chromatography (LC) coupled with mass spectrometry (MS). The FTIR spectra were used to detect the presence of pesticides in cocoa beans or lipid fraction (butter) by using a pattern recognition (Soft Independent Modeling by Class Analogy, SIMCA) algorithm, which produced a significant discrimination for cocoa nibs (free or with pesticides). The variables related to the class grouping were assigned to the aliphatic (3200–2800 cm−1) region with an interclass distance (ICD) of 3.3. Subsequently, the concentration of pesticides in cocoa beans was predicted using a partial least squares regression analysis (PLSR), using an internal validation of the PLRS model, the cross-validation correlation coefficient (Rval = 0.954) and the cross-validation standard error (SECV = 14.9 mg/kg) were obtained. Additionally, an external validation was performed, obtaining the prediction correlation coefficient (Rpre = 0.940) and the standard error of prediction (SEP = 16.0 μg/kg) with high statistical performances, which demonstrates the excellent predictability of the PLSR model in a similar real application. The developed FTIR method presented limits of detection and quantification (LOD = 9.8 μg/kg; LOQ = 23.1 μg/kg) with four optimum factors (PC). Mid-infrared spectroscopy (MIR) offered a viable alternative for field screening of cocoa. [Display omitted] •FTIR and chemometrics are a fast and economic way to determine pesticides in cocoa.•Portable FTIR instruments could monitor cocoa pesticides in the field.•SIMCA was used to classify cocoa beans for pesticide presence.•PLSR was used to quantify pesticide residues in cocoa beans.