Preliminary modeling of the visual quality of broccoli along the cold chain

Cefola, Maria; Bonis, Maria Valeria De; Pace, Bernardo

doi:10.1016/j.eaef.2016.11.005

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…Maria Cefola et al proposed that the important factor affecting the freshness of products is the technology of the cold chain. In modern society, advanced technology can accelerate production, shorten transportation time, reduce spoilage rate, and thus make the logistics cost reach the ideal value (Cefola et al, 2016). Considering the CCL technology, Forcinio put forward the temperature on this basis to specify the suitable temperature for the transportation and storage of fresh products, so as to ensure quality (Abad et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

An improved machine learning model Shapley value-based to forecast demand for aquatic product supply chain

Huang

2023

Front. Ecol. Evol.

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Previous machine learning models usually faced the problem of poor performance, especially for aquatic product supply chains. In this study, we proposed a coupling machine learning model Shapely value-based to predict the CCL demand of aquatic products (CCLD-AP). We first select the key impact indicators through the gray correlation degree and finally determine the indicator system. Secondly, gray prediction, principal component regression analysis prediction, and BP neural network models are constructed from the perspective of time series, linear regression and nonlinear, combined with three single forecasts, a combined forecasting model is constructed, the error analysis of all prediction model results shows that the combined prediction results are more accurate. Finally, the trend extrapolation method and time series are combined to predict the independent variable influencing factor value and the CCLD-AP from 2023 to 2027. Our study can provide a reference for the progress of CCLD-AP in ports and their hinterland cities.

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Section: Introductionmentioning

confidence: 99%