Alagappan Shanmugam, Hoffman Louwrens, Mikkelsen Deirdre, Mantilla Sandra Olarte, James Peter, Yarger Olympia, Cozzolino Daniel
Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Brisbane, QLD, Australia.
Fight Food Waste Cooperative Research Centre, Wine Innovation Central Building Level 1, Urrbrae, SA, Australia.
J Sci Food Agric. 2024 Feb;104(3):1487-1496. doi: 10.1002/jsfa.13044. Epub 2023 Oct 26.
The demand for protein obtained from animal sources is growing rapidly, as is the necessity for sustainable animal feeds. The use of black soldier fly larvae (BSFL) reared on organic side streams as sustainable animal feed has been receiving attention lately. This study assessed the ability of near-infrared spectroscopy (NIRS) combined with chemometrics to evaluate the nutritional profile of BSFL instars (fifth and sixth) and frass obtained from two different diets, namely soy waste and customised bread-vegetable diet. Partial least squares (PLS) regression with leave one out cross-validation was used to develop models between the NIR spectral data and the reference analytical methods.
Calibration models with good [coefficient of determination in calibration (R ): 0.90; ratio of performance to deviation (RPD) value: 3.6] and moderate (R : 0.76; RPD value: 2.1) prediction accuracy was observed for acid detergent fibre (ADF) and total carbon (TC), respectively. However, calibration models with moderate accuracy were observed for the prediction of crude protein (CP) (R : 0.63; RPD value: 1.4), crude fat (CF) (R : 0.70; RPD value: 1.6), neutral detergent fibre (NDF) (R : 0.60; RPD value: 1.6), starch (R : 0.52; RPD value: 1.4), and sugars (R : 0.52; RPD value: 1.4) owing to the narrow or uneven distribution of data over the range evaluated.
The near-infrared (NIR) calibration models showed a good to moderate prediction accuracy for the prediction of ADF and TC content for two different BSFL instars and frass reared on two different diets. However, calibration models developed for predicting CP, CF, starch, sugars and NDF resulted in models with limited prediction accuracy. © 2023 Society of Chemical Industry.
对动物源蛋白质的需求正在迅速增长，可持续动物饲料的需求也在增加。利用有机副产物饲养的黑水虻幼虫（BSFL）作为可持续动物饲料最近受到了关注。本研究评估了近红外光谱（NIRS）结合化学计量学评估从两种不同日粮（即大豆废料和定制面包 - 蔬菜日粮）获得的 BSFL 龄期（第五和第六龄）及虫粪营养成分的能力。采用留一法交叉验证的偏最小二乘（PLS）回归来建立近红外光谱数据与参考分析方法之间的模型。
分别观察到酸性洗涤纤维（ADF）和总碳（TC）的校准模型具有良好的[校准决定系数（R ）：0.90；性能与偏差比（RPD）值：3.6]和中等的（R ：0.76；RPD 值：2.1）预测准确性。然而，对于粗蛋白（CP）（R ：0.63；RPD 值：1.4）、粗脂肪（CF）（R ：0.70；RPD 值：1.6）、中性洗涤纤维（NDF）（R ：0.60；RPD 值：1.6）、淀粉（R ：0.52；RPD 值：1.4）和糖（R ：0.52；RPD 值：1.4）的预测，由于在所评估范围内数据分布狭窄或不均匀，观察到校准模型的准确性中等。
近红外（NIR）校准模型对两种不同日粮饲养 的两种不同 BSFL 龄期及虫粪的 ADF 和 TC 含量预测显示出良好到中等的预测准确性。然而，用于预测 CP、CF、淀粉、糖和 NDF 所建立的校准模型导致预测准确性有限。© 2023 化学工业协会
