椰子粉中蔗糖和葡萄糖添加量的快速检测

摘要

In this study,coconut powder provided by the factory as raw material,then sucrose and glucose was added to form a series of samples of sugar content gradient (2.5％ ～80％).This study based on infrared spectroscopy and near-infrared spectroscopy,the model of adding sucrose and glucose in coconut powder were established by partial least squares(PLS).And the calibration,cross validation and prediction were carried out.The results showed that infrared and near infrared models' calibration correlation coefficient (R2C),cross-validation correlation coefficient (R2CV),prediction correlation coefficient (R2P) of sucrose were 0.8752,0.8501,0.8401 and 0.9888,0.9868,0.9856 respectively.Infrared and near infrared models' calibration correlation coefficient (R2C),cross-validation correlation coefficient (R2CV),prediction correlation coefficient (R2P) of glucose were 0.8708,0.8500,0.8708 and 0.9569,0.9503,0.9572 respectively.The results indicated that the model of sucrose and glucose content of coconut powder established by infrared spectroscopy and near-infrared spectroscopy were good and the predicted value was close to the real added value.It feasible to rapidly detect the content of sucrose and glucose in coconut powder by infrared spectroscopy and near-infrared spectroscopy.In contrast,the model established by near-infrared spectroscopy would be better with more accurate prediction.%以椰子原粉为原料,分别添加蔗糖和葡萄糖形成一系列含糖量梯度(2.5％～80％)的椰子粉样品.分别运用红外光谱和近红外光谱技术,利用偏最小二乘法(PLS)建立椰子粉中添加蔗糖和葡萄糖含量的模型,并进行校正、交叉验证和预测.结果显示:蔗糖的红外和近红外模型的校正、交叉验证、预测的相关系数R2C、R2CV、R2P分别为0.8752、0.8501、0.8401和0.9888、0.9868、0.9856.葡萄糖的红外和近红外模型的校正、交叉验证、预测的相关系数R2C、R2CV、R2P分别为0.8708、0.8500、0.8708和0.9569、0.9503、0.9572.结果表明:利用红外光谱数据和近红外光谱数据建立的椰子粉蔗糖和葡萄糖含量的模型良好,预测值与真实值接近.说明利用红外光谱技术和近红外光谱技术快速检测椰子粉中添加蔗糖和葡萄糖含量的方法是可行的,且相比之下,利用近红外光谱数据建立的模型更优良,预测更准确.