Universidad Nacional Agraria La Molina, Graduate School (UNALM-EPG) Av. La Molina s/n, Lima 12, Peru; Departamento Académico de Ingeniería en Industrias Alimentarias e Ingeniería Forestal y Ambiental, Universidad Nacional Autónoma de Tayacaja Daniel Hernández Morillo (UNAT), Pampas, Huancavelica, Peru.
Universidad Nacional Agraria La Molina, Instituto de Investigación de Bioquímica y Biología Molecular (UNALM - IIBBM), Av. La Molina s/n, Lima 12, Peru.
Talanta. 2023 May 15;257:124386. doi: 10.1016/j.talanta.2023.124386. Epub 2023 Feb 18.
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) 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 (R = 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 (R = 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.
快速评估农药残留可确保可可豆的质量和市场适销性。在这项研究中，使用配备三重反射衰减全反射（ATR）附件的便携式傅里叶变换红外（FTIR）仪器筛选可可豆中的农药残留。从秘鲁主要可可种植区获得可可豆（n=75），并通过气相色谱（GC）或液相色谱（LC）与质谱（MS）联用定量分析农药残留。通过使用模式识别（软独立建模分类分析，SIMCA）算法，FTIR 光谱可用于检测可可豆或脂质部分（黄油）中农药的存在，该算法对可可豆仁（含或不含农药）产生了显著的区分。与类分组相关的变量被分配到脂肪族（3200-2800 cm）区域，类间距离（ICD）为 3.3。随后，使用偏最小二乘回归分析（PLSR）预测可可豆中农药的浓度，使用 PLSR 模型的内部验证，获得交叉验证相关系数（R=0.954）和交叉验证标准误差（SECV=14.9 mg/kg）。此外，进行了外部验证，获得预测相关系数（R=0.940）和预测标准误差（SEP=16.0 μg/kg），具有较高的统计性能，表明 PLSR 模型在类似的实际应用中具有出色的预测能力。所开发的 FTIR 方法在四个最佳因子（PC）下呈现出检测限和定量限（LOD=9.8 μg/kg；LOQ=23.1 μg/kg）。中红外光谱（MIR）为可可的现场筛选提供了可行的替代方案。
