Stacking ensemble learning for gas sensor‐based detection of salmon freshness and shelf life

Abstract Salmon, celebrated for its nutrition and flavor, suffers rapid degradation in freshness due to prolonged transportation and storage, offering a haven for microorganisms. Addressing the escalating need for safe, fresh fish consumption, we probed conventional chemical and physical indicators like total volatile basic nitrogen (TVB‐N), pH, texture profile analysis (TPA), and chromaticity across varying temperature intervals, linking these with gas sensors to identify sensitive sensor arrays. Importantly, an ensemble learning strategy for gas sensors, synthesizing the benefits of Linear, SVR, MLP, KNN, Gaussian Process, and decision tree algorithms, was employed for prompt and precise detection of salmon freshness and shelf‐life. Notably, the results demonstrated that gas sensors exhibited strong correlations, surpassing .8 for TVB‐N and .5 for shelf life, underscoring their aptitude for detecting salmon spoilage gas. Additionally, ensemble learning outperformed singular machine learning algorithms, with stacking emerging preeminent, achieving R 2 values of .851 and .871, and MSEs of .120 and 1.573, for TVB‐N and shelf‐life detection, respectively. In summation, this study introduces an avant‐garde mechanism that amplifies the detection efficacy of gas sensors for salmon freshness, marrying them with stacking ensemble learning paradigms for cost‐effective and efficient determinations. In conclusion, we devised a novel method to augment the detection efficacy of gas sensors for salmon freshness. By integrating these sensors with stacking ensemble learning algorithms, we achieved a data‐driven, cost‐effective, and efficient approach, fulfilling the requirements of salmon freshness detection. Practical applications Most existing gas sensors gas sensors predominantly employ singular machine learning methodologies, often limiting them to a sole freshness evaluation metric during assessments. This study introduces a pioneering approach using a stacking ensemble learning‐based gas sensor capable of concurrently assessing both TVB‐N and the shelf life of salmon. By discerning the correlations between freshness indices and gas sensor readings to pinpoint sensitive sensor arrays, we harnessed ensemble learning. This integrates the strengths of linear, SVR, MLP, KNN, Gaussian process, and decision tree models to enhance detection of freshness indicators. Notably, this advancement amplifies the sensor's efficacy in salmon detection solely through model optimization, bypassing the need to reconsider sensor materials and signal transmission pathways. Collectively, our findings present a cost‐effective and optimized strategy to elevate the performance of gas sensors in detecting salmon freshness.