Canneddu Giovanna, Júnior Luis Carlos Cunha, de Almeida Teixeira Gustavo Henrique
Dipt. di Agraria, Univ. degli Studi di Sassari, Via Enrico de Nicola, 7, 07.100, Sassari, Italy.
Univ. Federal de Goiás (UFG), Escola de Agronomia (EA), Setor de Horticultura. Avenida Esperança s/n - Campus Universitário, 74.690-000, Goiânia, GO, Brazil.
J Food Sci. 2016 Jul;81(7):C1613-21. doi: 10.1111/1750-3841.13343. Epub 2016 May 26.
The quality of shelled and unshelled macadamia nuts was assessed by means of Fourier transformed near-infrared (FT-NIR) spectroscopy. Shelled macadamia nuts were sorted as sound nuts; nuts infected by Ecdytolopha aurantiana and Leucopteara coffeella; and cracked nuts caused by germination. Unshelled nuts were sorted as intact nuts (<10% half nuts, 2014); half nuts (March, 2013; November, 2013); and crushed nuts (2014). Peroxide value (PV) and acidity index (AI) were determined according to AOAC. PCA-LDA shelled macadamia nuts classification resulted in 93.2% accurate classification. PLS PV prediction model resulted in a square error of prediction (SEP) of 3.45 meq/kg, and a prediction coefficient determination value (Rp (2) ) of 0.72. The AI PLS prediction model was better (SEP = 0.14%, Rp (2) = 0.80). Although adequate classification was possible (93.2%), shelled nuts must not contain live insects, therefore the classification accuracy was not satisfactory. FT-NIR spectroscopy can be successfully used to predict PV and AI in unshelled macadamia nuts, though.
采用傅里叶变换近红外(FT-NIR)光谱法对带壳和去壳澳洲坚果的品质进行了评估。去壳澳洲坚果被分为完好坚果;受金纹细蛾和咖啡潜叶蛾感染的坚果;以及因发芽导致开裂的坚果。带壳坚果被分为完整坚果(<10%半坚果,2014年);半坚果(2013年3月;2013年11月);以及破碎坚果(2014年)。根据美国官方分析化学家协会(AOAC)的方法测定了过氧化值(PV)和酸值(AI)。主成分分析-线性判别分析(PCA-LDA)对去壳澳洲坚果的分类准确率为93.2%。偏最小二乘法(PLS)PV预测模型的预测均方根误差(SEP)为3.45 meq/kg,预测系数决定值(Rp (2) )为0.72。AI的PLS预测模型效果更好(SEP = 0.14%,Rp (2) = 0.80)。虽然可以进行充分分类(93.2%),但去壳坚果不能含有活昆虫,因此分类准确率并不令人满意。不过,FT-NIR光谱法可以成功用于预测带壳澳洲坚果的PV和AI。