zhpsЖурнал прикладной спектроскопииZhurnal Prikladnoii Spektroskopii0514-7506B. I. Stepanov Institute of Physics of the National Academy of Scienceszhps-1096Research ArticleСтатьиОпределение содержания алюминия в мучных изделиях методом лазерной атомно-эмиссионной спектроскопии в сочетании с методом наименьших квадратовQuantitative Analysis of Al Inflour Products by Laser-Induced Breakdown Spectroscopy Combined with Partial Least SquaresDingQ.DingQ.

Наньчан

Nanchang

dqp0022@163.comYaoM.YaoM.

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Nanchang

WuSh.WuSh.

Наньчан

Nanchang

ZengM.ZengM.

Наньчан

Nanchang

XueN.XueN.

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Nanchang

WuD.WuD.

Наньчан

Nanchang

XuJ.XuJ.

Наньчан

Nanchang

Инженерный колледж Сельскохозяйственного университета ЦзянсиCollege of Engineering at Jiangxi Agricultural University202228072022894548554Copyright © Ding Q., Yao M., Wu S., Zeng M., Xue N., Wu D., Xu J., 20222022Ding Q., Yao M., Wu S., Zeng M., Xue N., Wu D., Xu J.Ding Q., Yao M., Wu S., Zeng M., Xue N., Wu D., Xu J.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://zhps.ejournal.by/jour/article/view/1096

С использованием метода наименьших квадратов (PLS) проведены сравнение и анализ влияния различных точек сглаживания и различных методов предварительной обработки на точность модели PLS. Показано, что количественная модель PLS дает наилучший эффект при использовании  11-точечного сглаживания в сочетании со стандартными нормальными переменными (SNVs) в качестве метода предварительной обработки. Коэффициенты детерминации Rc2 и Rp2 обучающего набора модели и набора прогнозирования составляют 0.9900 и 0.9996. Среднеквадратичные ошибки RMSECV = 11.7 и RMSEP = 5.23, средняя относительная ошибка прогнозирования 2.96% указывает на высокий уровень точности и точности прогнозирования. Продемонстрированы перспективы применения лазерной атомно-эмиссионной спектроскопии для количественного определения конкретных ингредиентов в мучных изделиях в качестве основы для мониторинга и оценки безопасности пищевых продуктов в режиме реального времени.

Based on partial least squares (PLS) analysis, the effects of different smoothing points and different preprocessing methods on the accuracy and precision of the PLS model were compared and analyzed. The results show that the PLS quantitative model has the best effect when using 11-point smoothing combined with standard normal variables (SNVs) as the preprocessing method. The coefficients of determination  (Rc2, Rp2) of the model training set and prediction set are 0.9900 and 0.9996, respectively. The root means square errors (RMSECV, RMSEP) are 11.7 and 5.23, respectively, and the average relative error of prediction is only 2.96%, which indicates a high prediction accuracy rate and accuracy. This work demonstrates that LIBS technology has broad application prospects for the quantitative detection of specific ingredients  in flour products and provides a basis for the real-time monitoring and evaluation of food safety. 

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The authors declare that there are no conflicts of interest present.