Nondestructive Evaluation of Banana Maturity Using NIR AS7263 Sensor

Kapse, Shrikant; Kedia, Priya; Kausley, Shankar B.; Rai, Beena

doi:10.1007/s10921-023-00943-z

Cited by 2 publications

(1 citation statement)

References 16 publications

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“…NIR has the characteristics of fast analysis speed and easy online real-time monitoring [16], meeting the needs of on-site measurement [17]. It is efficient, fast, and non-destructive [18], thus having great potential in the development of spectroscopic instruments [19]. Coppa et al [20] predicted the composition of milk fatty acids using NIR technology.…”

Section: Introductionmentioning

confidence: 99%

Portable Protein and Fat Detector in Milk Based on Multi-Spectral Sensor and Machine Learning

Wang,

Zhang,

Shi

et al. 2023

Applied Sciences

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To address the challenges of a long measurement period, high testing cost, and environmental pollution of traditional milk composition detection methods, a portable detection instrument was developed by combining multi-spectral sensors, machine learning algorithms, and an embedded system to rapidly detect the main components of milk. A broadband near-infrared (NIR) LED constant-current driver circuit and multi-spectral sensor module were designed to obtain six NIR features of milk samples. Based on a comparison of several machine learning algorithms, the XGBoost model was selected for training, and the trained model was ported to a Raspberry Pi unit for sample detection. The validation results showed that the coefficients of determination (R2) for the investigated protein and fat models were 0.9816 and 0.9978, respectively, and the corresponding mean absolute errors (MAE) were 0.0086 and 0.0079. Accurate measurement of protein and fat contents of milk can be facilitated in a short time interval by using the proposed low-cost portable instrument.

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