Carambola is a tropical fruit with rising value in developed countries due to its nutritional value and exotic aspect. It is important to assess carambola quality in different maturity stages to estimate a “fair price” and to assign fruit for specific applications and markets. This work reported the use of a portable NIR spectrometer in the range of 900 to 1700 nm as a non-destructive, chemical-free technique for determination of carambola physicochemical properties, according to maturity stage. Colour, total soluble solids, ascorbic acid, moisture, pH and titratable acidity analysis were performed for 177 fruit from two clones and four maturity stages (MS1, MS2, MS3 and MS4). PLS-DA and PLSR models were built to classify carambola according to maturity stage and to estimate its physicochemical properties, respectively. Several pre-processing were tested and among them the new algorithm introduced in the (SNV) pre-processing, the variable sorting for normalization (VSN), allows the improvement of the signal shape and the model interpretation. Genetic algorithm (GA) and interval partial least square (iPLS) were tested for improving model performance. The PLS-DA model based on important variables selected by iPLS achieved the best performance with 84.2% accuracy to classify carambola according to maturity stage. Variable selection (iPLS and GA-PLS) allowed an improvement in the performance of the PLSR models, with pH and moisture content achieving (R²_P of 0.78 and 0.74), (RMSEP of 0.2 and 0.87), (RPD of 2.01 and 2.23) and (RER of 8.02 and 10.38), respectively, which is acceptable for screening. Portable NIR spectrometer, which can be considered low-cost when compared to benchtop spectrometers, in tandem with chemometrics can be a promising tool to assess the composition and to classify carambola according to maturity stage.

杨桃是一种热带水果，由于其营养价值和异国情调，在发达国家价值不断上升。评估不同成熟阶段的杨桃质量以估算“公平价格”并为特定应用和市场分配水果非常重要。这项工作报告了使用 900 至 1700 nm 范围内的便携式 NIR 光谱仪作为一种非破坏性、无化学物质的技术，用于根据成熟阶段确定杨桃的物理化学性质。对来自两个克隆和四个成熟阶段（MS1、MS2、MS3 和 MS4）的 177 个水果进行颜色、总可溶性固体、抗坏血酸、水分、pH 和可滴定酸度分析。建立PLS-DA和PLSR模型，分别根据成熟期对杨桃进行分类并估计其理化性质。对几个预处理进行了测试，其中在（SNV）预处理中引入的新算法，标准化变量排序（VSN）允许改进信号形状和模型解释。测试了遗传算法 (GA) 和区间偏最小二乘法 (iPLS) 以提高模型性能。基于 iPLS 选择的重要变量的 PLS-DA 模型以 84.2% 的准确率实现了最佳性能，以根据成熟度对杨桃进行分类。变量选择（iPLS 和 GA-PLS）提高了 PLSR 模型的性能，pH 和水分含量达到（R 测试了遗传算法 (GA) 和区间偏最小二乘法 (iPLS) 以提高模型性能。基于 iPLS 选择的重要变量的 PLS-DA 模型以 84.2% 的准确率实现了最佳性能，以根据成熟度对杨桃进行分类。变量选择（iPLS 和 GA-PLS）提高了 PLSR 模型的性能，pH 和水分含量达到（R 测试了遗传算法 (GA) 和区间偏最小二乘法 (iPLS) 以提高模型性能。基于 iPLS 选择的重要变量的 PLS-DA 模型以 84.2% 的准确率实现了最佳性能，以根据成熟度对杨桃进行分类。变量选择（iPLS 和 GA-PLS）提高了 PLSR 模型的性能，pH 和水分含量达到（R² _P分别为 0.78 和 0.74）、（RMSEP 为 0.2 和 0.87）、（RPD 为 2.01 和 2.23）和（RER 分别为 8.02 和 10.38），这对于筛选是可以接受的。与台式光谱仪相比，便携式 NIR 光谱仪可以被认为是低成本的，与化学计量学相结合，可以成为评估成分和根据成熟阶段对杨桃进行分类的有前途的工具。