Pre-dispersive near-infrared light sensing in non-destructively classifying the brix of intact pineapples

• Main Authors: Chia, KS (Author), Gan, Z (Author), Ismail, IN (Author), Jam, MNH (Author)
• Format: Article
• Language: English
• Published: 2020
• Subjects: Artificial neural network; Brix; Non-destructive measurement; Pineapples; PREDICTION; Pre-dispersive Near-infrared; QUALITY ASSESSMENT; SENSITIVITY-ANALYSIS; SOLUBLE SOLID CONTENT
• Online Access: View Fulltext in Publisher

 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.