Wang Bo, Han Junying, Liu Chengzhong, Zhang Jianping, Qi Yanni
College of Information Science and Technology, Gansu Agricultural University, Lanzhou, China.
Crop Research Institute, Gansu Academy of Agricultural Sciences, Lanzhou, China.
Front Nutr. 2025 Apr 15;12:1551029. doi: 10.3389/fnut.2025.1551029. eCollection 2025.
The protein content of flaxseed () is a crucial factor influencing its nutritional value and quality. Spectral technology combined with advanced modeling methods offers a fast, accurate, and cost-effective approach for predicting protein content. In this study, visible-near infrared hyperspectral imaging (VNIR-HIS) technology was combined with fractional order ant colony optimization (FOACO) to determine the protein content of flaxseed. Thirty flaxseed varieties commonly cultivated in Northwest China were selected, and hyperspectral data along with protein content measurements were collected. A joint x-y distance algorithm was applied to divide the dataset into calibration and prediction sets after removing outliers. Partial least squares regression (PLSR) models were developed based on both raw and preprocessed spectra, with the Savitzky-Golay (SG) smoothing method found to provide superior performance. The performance of wavelength selection methods based on FOACO, principal component analysis (PCA), and ant colony optimization (ACO) was compared using PLSR and multiple linear regression (MLR) models. The FOACO-MLR model achieved a prediction accuracy of 0.9248, a root mean square error (RMSE) of 0.4346, a relative prediction deviation (RPD) of 3.6458, and a mean absolute error (MAE) of 0.3259. The results show that the FOACO-MLR model provides significant advantages in predicting flaxseed protein content, particularly in terms of prediction accuracy and stability of characteristic bands. By combining VNIR-HIS technology with the FOACO wavelength selection algorithm, this study offers an efficient and rapid method for determining the protein content of flaxseed, providing reliable technical support for the precise detection of nutritional components.
亚麻籽的蛋白质含量是影响其营养价值和品质的关键因素。光谱技术与先进的建模方法相结合，为预测蛋白质含量提供了一种快速、准确且经济高效的方法。在本研究中，可见 - 近红外高光谱成像（VNIR - HIS）技术与分数阶蚁群优化（FOACO）相结合，以测定亚麻籽的蛋白质含量。选取了中国西北地区常见种植的30个亚麻籽品种，收集了高光谱数据以及蛋白质含量测量值。在去除异常值后，应用联合x - y距离算法将数据集划分为校准集和预测集。基于原始光谱和预处理光谱建立了偏最小二乘回归（PLSR）模型，发现Savitzky - Golay（SG）平滑方法具有更好的性能。使用PLSR和多元线性回归（MLR）模型比较了基于FOACO、主成分分析（PCA）和蚁群优化（ACO）的波长选择方法的性能。FOACO - MLR模型的预测准确率为0.9248，均方根误差（RMSE）为0.4346，相对预测偏差（RPD）为3.6458，平均绝对误差（MAE）为0.3259。结果表明，FOACO - MLR模型在预测亚麻籽蛋白质含量方面具有显著优势，特别是在特征波段的预测准确性和稳定性方面。通过将VNIR - HIS技术与FOACO波长选择算法相结合，本研究提供了一种高效快速的亚麻籽蛋白质含量测定方法，为营养成分的精确检测提供了可靠的技术支持。
