The Sensor Modules of a Dedicated Automatic Inspection System for Screening Smoked Sausage Coloration

The external color of smoked sausages is a critical indicator of quality and uniformity in processing. Commercial colorimeters are unsuitable for high-throughput sorting due to the challenges posed by the sausage's curved cylindrical surface and the need for an inline application. This study introduces a novel non-contact sensing module (LEDs at 45°, fiber optic collection at 0°) to acquire spectral data (400-700 nm) and derive CIE LAB. First, a handheld prototype validated the accuracy of the sensing module against a benchtop spectrophotometer. It successfully categorized five color grades ('Over light', 'Light', 'Standard', 'Dark', and 'Over dark') with a clear distribution on the a*-L* diagram. This established acceptable color boundary conditions (44.2 < L* ≤ 61.3, 14.1 < a* < 23.9). Second, three sensing modules were integrated around a conveyor belt at 120° intervals, forming the core of an automated inline sorting system. Blind field tests (<i>n</i> = 150) achieved high sorting accuracies of 95.3-97.3% with an efficient inspection time of less than 2 s per sausage. This work realizes the standardization, digitalization, and automation of food color inspection, demonstrating strong potential for smart manufacturing in the processed meat industry.

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Publication Details

Journal
Sensors
Published
2026-01-20
DOI
https://doi.org/10.3390/s26020678
Primary Topic
Spectroscopy and Chemometric Analyses
Type
article
Field-Weighted Citation Impact
0.00

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article

The Sensor Modules of a Dedicated Automatic Inspection System for Screening Smoked Sausage Coloration

Yen-Hsiang Wang, Jen-Jie Chieh, Kuan-Chieh Lee, Chin-Cheng Wu et al.
Sensors
Spectroscopy and Chemometric Analyses
article

The Sensor Modules of a Dedicated Automatic Inspection System for Screening Smoked Sausage Coloration

Yen-Hsiang Wang, Jen-Jie Chieh, Kuan-Chieh Lee, Chin-Cheng Wu, Yu-Fen Yen, Meng-Jen Tsai, Ching-Yuan Chang
article en

Abstract

The external color of smoked sausages is a critical indicator of quality and uniformity in processing. Commercial colorimeters are unsuitable for high-throughput sorting due to the challenges posed by the sausage's curved cylindrical surface and the need for an inline application. This study introduces a novel non-contact sensing module (LEDs at 45°, fiber optic collection at 0°) to acquire spectral data (400-700 nm) and derive CIE LAB. First, a handheld prototype validated the accuracy of the sensing module against a benchtop spectrophotometer. It successfully categorized five color grades ('Over light', 'Light', 'Standard', 'Dark', and 'Over dark') with a clear distribution on the a*-L* diagram. This established acceptable color boundary conditions (44.2 < L* ≤ 61.3, 14.1 < a* < 23.9). Second, three sensing modules were integrated around a conveyor belt at 120° intervals, forming the core of an automated inline sorting system. Blind field tests (<i>n</i> = 150) achieved high sorting accuracies of 95.3-97.3% with an efficient inspection time of less than 2 s per sausage. This work realizes the standardization, digitalization, and automation of food color inspection, demonstrating strong potential for smart manufacturing in the processed meat industry.

SensorsVol. 26(2)
National Taiwan Normal University (TW), Food Industry Research and Development Institute (TW), National Ilan University (TW)
Ministry of Economic Affairs, National Science and Technology Council
Openalex Percentile: Top 82%
Spectroscopy and Chemometric Analyses
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