Goi Arianna, Simoni Marica, Righi Federico, Visentin Giulio, De Marchi Massimo
Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy.
Department of Veterinary Science, University of Parma, Via del Taglio 10, 43126 Parma, Italy.
Animals (Basel). 2020 Sep 16;10(9):1660. doi: 10.3390/ani10091660.
The aim of the present study was to investigate the ability of a handheld near-infrared spectrometer to predict total and gelatinized starch, insoluble fibrous fractions, and mineral content in extruded dry dog food. Intact and ground samples were compared to determine if the homogenization could improve the prediction performance of the instrument. Reference analyses were performed on 81 samples for starch and 99 for neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent lignin (ADL), and minerals, and reflectance infrared spectra (740 to 1070 nm) were recorded with a SCiO™ near-infrared (NIR) spectrometer. Prediction models were developed using modified partial least squares regression and both internal (leave-one-out cross-validation) and external validation. The best prediction models in cross-validation using ground samples were obtained for gelatinized starch (residual predictive deviation, RPD = 2.54) and total starch (RPD = 2.33), and S (RPD = 1.92), while the best using intact samples were obtained for gelatinized starch (RPD = 2.45), total starch (RPD = 2.08), and K (RPD = 1.98). Through external validation, the best statistics were obtained for gelatinized starch, with an RPD of 2.55 and 2.03 in ground and intact samples, respectively. Overall, there was no difference in prediction models accuracy using ground or intact samples. In conclusion, the miniaturized NIR instrument offers the potential for screening purposes only for total and gelatinized starch, S, and K, whereas the results do not support its applicability for the other traits.
本研究的目的是调查手持式近红外光谱仪预测挤压干狗粮中总淀粉、糊化淀粉、不溶性纤维部分和矿物质含量的能力。将完整样品和研磨样品进行比较，以确定均质化是否可以提高仪器的预测性能。对81个淀粉样品和99个中性洗涤纤维（NDF）、酸性洗涤纤维（ADF）、酸性洗涤木质素（ADL）和矿物质样品进行了参考分析，并用SCiO™近红外（NIR）光谱仪记录了反射红外光谱（740至1070nm）。使用改进的偏最小二乘回归以及内部（留一法交叉验证）和外部验证开发了预测模型。使用研磨样品进行交叉验证时，糊化淀粉（剩余预测偏差，RPD = 2.54）、总淀粉（RPD = 2.33）和硫（RPD = 1.92）获得了最佳预测模型，而使用完整样品时，糊化淀粉（RPD = 2.45）、总淀粉（RPD = 2.08）和钾（RPD = 1.98）获得了最佳预测模型。通过外部验证，糊化淀粉获得了最佳统计数据，研磨样品和完整样品的RPD分别为2.55和2.03。总体而言，使用研磨样品或完整样品的预测模型准确性没有差异。总之，小型近红外仪器仅对总淀粉和糊化淀粉、硫和钾具有用于筛选目的的潜力，而结果不支持其对其他特性的适用性。
