用近红外光谱和多变量分析预测鸡肉肉丸的品质

Meat Research Pub Date : 2022-10-30 DOI:10.55002/mr.2.5.34
M. Hashem, M. Morshed, M. Khan, Md. Mizanur Rahman, M. A. Noman, A. Mustari, P. Goswami
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引用次数: 1

摘要

近红外(NIR)光谱学有很大的机会取代昂贵和耗时的化学传统分析来确定肉制品的质量。本研究旨在评价近红外光谱法的可行性,建立一种快速、简便的鸡肉肉丸质量评价方法。肉丸样品(n=123)来自孟加拉国嘉禾公司。采集样品后,在分析前获取光谱,通过DLP近红外扫描纳米软件共收集369个近红外光谱并存储在计算机中。为了生成参考数据,我们分析了123个肉丸样品的近似成分、仪器颜色CIE L*、a*、b*和肉丸的pH值。然后利用The unscbler X软件建立偏最小二乘回归模型进行校正和交叉验证,进行数据分析。采用校正均方根误差(RMSEC)、交叉验证均方根误差(RMSECV)、校正系数(R²C)和交叉验证系数(R2 CV)对校正模型的精度进行评价。利用偏最小二乘回归从参考数据推导出校准方程。L*、a*、b*、pH、DM、水分、CP、EE和灰分的标准偏差分别为2.41、0.14、2.1、0.41、1.31、0.31、1.26、0.38和0.38,表明所有值都足以用于分析目的。通过交叉验证决定系数(R2 CV)和交叉验证均方根误差评价模型的预测能力。对亮度(L*) (R2 CV=0.84)、红度(a*) (R2 CV=0.72)、黄度(b*) (R2 CV=0.77)、pH (R2 CV=0.78)、CP (R2 CV=0.73)、EE (R2 CV=0.83) (R2 CV=0.72)、水分(R2 CV=0.72)、DM (R2 CV=0.72)和灰分(R2 CV=0.74)的预测都很好。结果表明,近红外光谱技术可用于鸡肉肉丸理化性状的快速评价。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of chicken meatball quality through NIR spectroscopy and multivariate analysis
Near Infrared (NIR) Spectroscopy leads a great opportunity to replace the expensive and time-consuming chemical conventional analysis for determination of the quality of meat products. This study was conducted aiming to evaluate the feasibility of NIRS and to establish a rapid assessment method to easily predict the quality of chicken meatball. Samples of meatball (n=123) were collected from Golden Harvest Company of Bangladesh. After collecting sample, spectra were obtained prior to analysis and a total of 369 NIRs were collected and stored in computer by DLP NIR scan Nano Software. To generate reference data 123 meatball samples were analyzed for proximate components, instrumental color CIE L*, a*, b*, and pH of meatball. After that a partial least square regression model for calibration and cross validation were developed for data analysis using The Unscrambler X software. Accuracies of the calibration models were evaluated using the root mean square error of calibration (RMSEC), root mean square error of cross-validation (RMSECV), coefficient of calibration (R²C) and coefficient of cross validation (R2 CV). Calibration equations were developed from reference data using partial least squares regressions. The standard deviation is 2.41, 0.14, 2.1, 0.41, 1.31, 0.31, 1.26, 0.38, and 0.38 for L*, a*, b*, pH, DM, moisture, CP, EE and ash respectively which indicates that all values are adequate for analytical purposes. Predictive ability of the models was assessed by coefficient of determination of cross-validation (R2 CV) and root mean square error of cross-validation. Predictions were good (R2 CV=0.84) for lightness (L*), (R2 CV=0.72) for redness (a*), (R2 CV=0.77) for yellowness (b*), (R2 CV=0.78) for pH, (R2 CV=0.73) for CP, (R2 CV=0.83) for EE (R2 CV=0.72) for moisture, (R2 CV=0.72) for DM and (R2 CV=0.74) for ash. From the results, it can be concluded that NIRS can be used for the rapid assessment of physico-chemical traits of chicken meatball.
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