Sánchez María-Teresa, Pérez-Marín Dolores, Flores-Rojas Katherine, Guerrero José-Emilio, Garrido-Varo Ana
Departamento de Bromatología y Tecnología de los Alimentos, Universidad de Cordoba, Edificio Charles Darwin, 14071 Cordoba, Spain.
Talanta. 2009 Apr 30;78(2):530-6. doi: 10.1016/j.talanta.2008.12.004. Epub 2008 Dec 6.
This study sought to evaluate the ability of near-infrared reflectance spectroscopy (NIRS) to classify intact green asparagus, in refrigerated storage under controlled atmosphere, by storage time and post-harvest treatments applied. A total of 468 green asparagus (Asparagus officinalis, L., cultivar UC-157) were sampled after 7, 14, 21 and 28 days of refrigerated storage (2 degrees C, 95% R.H.) under three controlled atmosphere (CA) treatments: air (21 kPa O(2)+0.3 kPa CO(2)), CA(1) (5 kPa O(2)+5 kPa CO(2)) and CA(2) (10 kPa O(2)+10kPa CO(2)). Two commercially available spectrophotometers were evaluated for this purpose: a scanning monochromator (SM) of 400-2500 nm and a combination of diode array and scanning monochromator (DASM) of 350-2500 nm. Models developed using partial least squares 2-discriminant analysis (PLS2-DA) correctly classified between 81-100% of samples by post-harvest storage time, depending on the instrument used. Using similar models, the DASM instrument correctly classified 85% of samples by post-harvest treatment, compared with 72% using the SM. These results confirmed that NIR spectroscopy, coupled with the use of chemometric techniques, provides a reliable, accurate method of predicting the shelf-life of asparagus under different storage conditions and as a function of post-harvest treatment applied; the method can be readily applied at industrial level.
本研究旨在评估近红外反射光谱法(NIRS)对处于可控气氛冷藏条件下的完整绿芦笋,根据储存时间和所采用的采后处理方式进行分类的能力。在三种可控气氛(CA)处理条件下,对468根绿芦笋(芦笋,L.,品种UC - 157)在冷藏储存(2℃,95%相对湿度)7、14、21和28天后进行采样:空气(21 kPa O₂ + 0.3 kPa CO₂)、CA(1)(5 kPa O₂ + 5 kPa CO₂)和CA(2)(10 kPa O₂ + 10 kPa CO₂)。为此评估了两台市售分光光度计:一台400 - 2500 nm的扫描单色仪(SM)和一台350 - 2500 nm的二极管阵列与扫描单色仪组合(DASM)。使用偏最小二乘判别分析(PLS2 - DA)开发的模型,根据所使用的仪器不同,能正确分类81% - 100%的不同采后储存时间的样品。使用类似模型,DASM仪器能正确分类85%的经采后处理的样品,而使用SM仪器的正确率为72%。这些结果证实,近红外光谱法结合化学计量技术,提供了一种可靠、准确的方法来预测芦笋在不同储存条件下以及作为采后处理函数的货架期;该方法可在工业层面轻松应用。