Exported fresh intact pineapples must fulfill the minimum internal quality requirement of 12 degree brix. Even though near-infrared (NIR) spectroscopic approaches are promising to non-destructively and rapidly assess the internal quality of intact pineapples, these approaches involve expensive and complex NIR spectroscopic instrumentation. Thus, this research evaluates the performance of a proposed pre-dispersive NIR light sensing approach in non-destructively classifying the Brix of pineapples using K-fold cross-validation, holdout validation, and sensitive analysis. First, the proposed pre-dispersive NIR sensing device that consisted of a light sensing element and five NIR light emitting diodes with peak wavelengths of 780, 850, 870, 910, and 940 nm, respectively, was developed. After that, the diffuse reflectance NIR light of intact pineapples was non-destructively acquired using the developed NIR sensing device before their Brix values were conventionally measured using a digital refractometer. Next, an artificial neural network (ANN) was trained and optimized to classify the Brix values of pineapples using the acquired NIR light. The results of the sensitivity analysis showed that either one wavelength that was near to the water absorbance or chlorophyll band was redundant in the classification. The performance of the trained ANN was tested using new pineapples with the optimal classification accuracy of 80.56%. This indicates that the proposed pre-dispersive NIR light sensing approach coupled with the ANN is promising to be an alternative to non-destructively classifying the internal quality of fruits.

出口的新鲜完整菠萝必须满足 12 度白利糖度的最低内部质量要求。尽管近红外 (NIR) 光谱方法有望以无损方式快速评估完整菠萝的内部质量，但这些方法涉及昂贵且复杂的 NIR 光谱仪器。因此，本研究评估了所提出的预色散 NIR 光传感方法在使用 K 折交叉验证、坚持验证和敏感分析对菠萝的白利糖度进行非破坏性分类方面的性能。首先，开发了所提出的预色散 NIR 传感装置，该装置由一个光传感元件和五个峰值波长分别为 780、850、870、910 和 940 nm 的 NIR 发光二极管组成。在那之后，在使用数字折射计常规测量其白利糖度值之前，使用开发的 NIR 传感设备无损地获取完整菠萝的漫反射 NIR 光。接下来，训练和优化人工神经网络 (ANN) 以使用获取的 NIR 光对菠萝的白利糖度值进行分类。敏感性分析结果表明，接近吸水率或叶绿素波段的一个波长在分类中是多余的。使用新菠萝测试训练后的 ANN 的性能，最佳分类准确率为 80.56%。这表明所提出的预色散 NIR 光传感方法与 ANN 相结合，有望成为对水果内部质量进行非破坏性分类的替代方案。