PEACCEL, Protein Engineering Accelerator, 6 square Albin Cachot, box 42, 75013, Paris, France.
LSE laboratory, EPITA, Paris, 94276, France.
Sci Rep. 2019 Dec 16;9(1):19197. doi: 10.1038/s41598-019-55543-7.
Fast advancement of machine learning methods and constant growth of the areas of application open up new horizons for large data management and processing. Among the various types of data available for analysis, the Fourier Transform InfraRed (FTIR) spectroscopy spectra are very challenging datasets to consider. In this study, machine learning is used to analyze and predict a rheological parameter: firmness. Various statistics have been gathered including both chemistry (such as ethylene, titrable acidity or sugars) and spectra values to visualize and analyze a dataset of 731 biological samples. Two-dimensional (2D) and three-dimensional (3D) principal component analyses (PCA) are used to evaluate their ability to discriminate for one parameter: firmness. Partial least squared regression (PLSR) modeling has been carried out to predict the rheological parameter using either sixteen physicochemical parameters or only the infrared spectra. We show that (i) the spectra alone allows good discrimination of the samples based on rheology, (ii) 3D-PCA allows comprehensive and informative visualization of the data, and (iii) that the rheological parameters are predicted accurately using a regression method such as PLSR; instead of using chemical parameters which are laborious to obtain, Mid-FTIR spectra gathering all physicochemical information could be used for efficient prediction of firmness. As a conclusion, rheological and chemical parameters allow good discrimination of the samples according to their firmness. However, using only the IR spectra leads to better results. A good predictive model was built for the prediction of the firmness of the fruit, and we reached a coefficient of determination R value of 0.90. This method outperforms a model based on physicochemical descriptors only. Such an approach could be very helpful to technologists and farmers.
机器学习方法的快速发展和应用领域的不断增长为大数据管理和处理开辟了新的前景。在可用于分析的各种类型的数据中，傅里叶变换红外（FTIR）光谱是非常具有挑战性的数据集。在本研究中，使用机器学习来分析和预测流变学参数：硬度。收集了各种统计数据，包括化学数据（如乙烯、可滴定酸度或糖）和光谱值，以可视化和分析 731 个生物样本的数据集。二维（2D）和三维（3D）主成分分析（PCA）用于评估它们区分一个参数（硬度）的能力。进行了偏最小二乘回归（PLSR）建模，使用十六个物理化学参数或仅使用红外光谱来预测流变学参数。我们表明：（i）仅光谱就可以根据流变学很好地区分样本，（ii）3D-PCA 允许全面和信息丰富地可视化数据，以及（iii）使用回归方法（如 PLSR）可以准确预测流变学参数；而不是使用费力获得的化学参数，可以使用收集所有物理化学信息的中红外光谱来有效地预测硬度。总之，流变学和化学参数可以根据硬度很好地区分样本。然而，仅使用 IR 光谱会产生更好的结果。建立了一个用于预测果实硬度的良好预测模型，我们达到了 0.90 的决定系数 R 值。这种方法优于仅基于物理化学描述符的模型。这种方法可能对技术人员和农民非常有帮助。
