Sousa Massaine Bandeira E, Filho Juraci Souza Sampaio, de Andrade Luciano Rogerio Braatz, de Oliveira Eder Jorge
Embrapa Mandioca e Fruticultura, Cruz das Almas, Bahia, Brazil.
Universidade Federal do Recôncavo da Bahia, Cruz das Almas, Bahia, Brazil.
Front Plant Sci. 2023 Jan 23;14:1089759. doi: 10.3389/fpls.2023.1089759. eCollection 2023.
Cassava ( Crantz) starch consists of amylopectin and amylose, with its properties determined by the proportion of these two polymers. Waxy starches contain at least 95% amylopectin. In the food industry, waxy starches are advantageous, with pastes that are more stable towards retrogradation, while high-amylose starches are used as resistant starches. This study aimed to associate near-infrared spectrophotometry (NIRS) spectra with the waxy phenotype in cassava seeds and develop an accurate classification model for indirect selection of plants. A total of 1127 F seeds were obtained from controlled crosses performed between 77 F genotypes (wild-type, _). Seeds were individually identified, and spectral data were obtained NIRS using a benchtop NIRFlex N-500 and a portable SCiO device spectrometer. Four classification models were assessed for waxy cassava genotype identification: k-nearest neighbor algorithm (KNN), C5.0 decision tree (CDT), parallel random forest (parRF), and eXtreme Gradient Boosting (XGB). Spectral data were divided between a training set (80%) and a testing set (20%). The accuracy, based on NIRFlex N-500 spectral data, ranged from 0.86 (parRF) to 0.92 (XGB). The Kappa index displayed a similar trend as the accuracy, considering the lowest value for the parRF method (0.39) and the highest value for XGB (0.71). For the SCiO device, the accuracy (0.88-0.89) was similar among the four models evaluated. However, the Kappa index was lower than that of the NIRFlex N-500, and this index ranged from 0 (parRF) to 0.16 (KNN and CDT). Therefore, despite the high accuracy these last models are incapable of correctly classifying waxy and non-waxy clones based on the SCiO device spectra. A confusion matrix was performed to demonstrate the classification model results in the testing set. For both NIRS, the models were efficient in classifying non-waxy clones, with values ranging from 96-100%. However, the NIRS differed in the potential to predict waxy genotype class. For the NIRFlex N-500, the percentage ranged from 30% (parRF) to 70% (XGB). In general, the models tended to classify waxy genotypes as non-waxy, mainly SCiO. Therefore, the use of NIRS can perform early selection of cassava seeds with a waxy phenotype.
木薯（Crantz）淀粉由支链淀粉和直链淀粉组成，其性质由这两种聚合物的比例决定。糯性淀粉至少含有95%的支链淀粉。在食品工业中，糯性淀粉具有优势，其糊化产物对回生更稳定，而高直链淀粉则用作抗性淀粉。本研究旨在将近红外分光光度法（NIRS）光谱与木薯种子的糯性表型相关联，并开发一种用于间接选择植株的准确分类模型。通过77个F基因型（野生型，_）之间进行的控制杂交获得了总共1127粒F种子。对种子进行单独识别，并使用台式NIRFlex N - 500和便携式SCiO设备光谱仪通过NIRS获取光谱数据。评估了四种用于识别木薯糯性基因型的分类模型：k近邻算法（KNN）、C5.0决策树（CDT）、并行随机森林（parRF）和极端梯度提升（XGB）。光谱数据被分为训练集（80%）和测试集（20%）。基于NIRFlex N - 500光谱数据的准确率在0.86（parRF）至0.92（XGB）之间。考虑到parRF方法的最低值（0.39）和XGB的最高值（0.71），卡帕指数呈现出与准确率相似的趋势。对于SCiO设备，在所评估的四个模型中准确率（0.88 - 0.89）相似。然而，卡帕指数低于NIRFlex N - 500，该指数范围从0（parRF）至0.16（KNN和CDT）。因此，尽管这些模型准确率较高，但基于SCiO设备光谱无法正确区分糯性和非糯性克隆。通过混淆矩阵展示了测试集中分类模型的结果。对于两种NIRS，模型在分类非糯性克隆方面效率较高，值范围为96 - 100%。然而，NIRS在预测糯性基因型类别方面的潜力有所不同。对于NIRFlex N - 500，百分比范围从30%（parRF）至70%（XGB）。总体而言，模型倾向于将糯性基因型分类为非糯性，主要是SCiO设备。因此，使用NIRS可以对具有糯性表型的木薯种子进行早期选择。
