Dataset Information

Intelligent non-colorimetric indicators for the perishable supply chain by non-wovens with photo-programmed thermal response.

ABSTRACT: Spoiled perishable products, such as food and drugs exposed to inappropriate temperature, cause million illnesses every year. Risks range from intoxication due to pathogen-contaminated edibles, to suboptimal potency of temperature-sensitive vaccines. High-performance and low-cost indicators are needed, based on conformable materials whose properties change continuously and irreversibly depending on the experienced time-temperature profile. However, these systems can be limited by unclear reading, especially for colour-blind people, and are often difficult to be encoded with a tailored response to detect excess temperature over varying temporal profiles. Here we report on optically-programmed, non-colorimetric indicators based on nano-textured non-wovens encoded by their cross-linking degree. This combination allows a desired time-temperature response to be achieved, to address different perishable products. The devices operate by visual contrast with ambient light, which is explained by backscattering calculations for the complex fibrous material. Optical nanomaterials with photo-encoded thermal properties might establish new design rules for intelligent labels.

SUBMITTER: Romano L

PROVIDER: S-EPMC7688929 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Intelligent non-colorimetric indicators for the perishable supply chain by non-wovens with photo-programmed thermal response.

Romano Luigi L Portone Alberto A Coltelli Maria-Beatrice MB Patti Francesco F Saija Rosalba R Iatì Maria Antonia MA Gallone Giuseppe G Lazzeri Andrea A Danti Serena S Maragò Onofrio M OM Camposeo Andrea A Pisignano Dario D Persano Luana L

Nature communications 20201125 1

Spoiled perishable products, such as food and drugs exposed to inappropriate temperature, cause million illnesses every year. Risks range from intoxication due to pathogen-contaminated edibles, to suboptimal potency of temperature-sensitive vaccines. High-performance and low-cost indicators are needed, based on conformable materials whose properties change continuously and irreversibly depending on the experienced time-temperature profile. However, these systems can be limited by unclear reading ...[more]

PMID: 33239625

Similar Datasets

Project description:In recent decades, changes in people's lifestyles and occupations have led to new food consumption patterns around the world, with a notable growth in the demand for ready meals and meat products. Accordingly, the food industry has tried to transform the global food culture by promoting its more profitable products to become able to set a higher price for meat. Considering the short lifetime of products in perishable food supply chains, inventory decisions are considered crucial. In addition, the demand for perishable food products is greatly affected by their freshness. In this paper, we develop a multi-objective mixed-integer non-linear programming model for a four-level sustainable supply chain (SC) of a perishable product with price-dependent demand and deterioration rates. The SC consists of suppliers, a production center (PC), distribution centers (DCs), and retailers. We aim to ascertain the optimal pricing policy and cycle length to maximize profit, achieve specific social objectives, and minimize the total cost and adverse environmental impacts. The proposed model determines the deterioration rate of each product according to its expiration date, the optimal location of supply-side facilities, and the flow rate between the facilities based on the selected mode of delivery. The products are delivered to the retailers by a mechanized transportation system either directly from the PC or indirectly through the DCs. Finally, an actual case study is provided to demonstrate the applicability of the model and our theoretical results under real-world conditions. We solve the case study by a preemptive fuzzy goal programming method and perform several sensitivity analyses on the results. By performing simultaneous sensitivity analyses on the demand and expiration dates, the optimal values of the problem's parameters are determined. The optimal values help decision-makers make optimal decisions regarding the selling price of products and replenishment times. The model is applicable to supply chains of any perishable items with expiration dates.

Project description:BackgroundReducing food loss and waste (FLW) may narrow gaps between fruit and vegetable production and recommended intake. However, FLW estimates are inconsistent due to varying estimation methods.ObjectivesUsing multiple estimation approaches, we examined the extent and determinants of FLW along tomato supply chains in South India, from farm to retail. We also explored tomato quality assessments.MethodsWe surveyed 75 farm households and 83 tomato traders in the Chittoor district, Andhra Pradesh, and 52 vegetable traders and 50 vegetable retailers in Hyderabad, Telangana, on harvest and market days. We calculated declared FLW values using participant-reported losses to estimate the preharvest quality FLW and quantitative FLW values at the farmer, vegetable-trader, and vegetable-retailer stages. We calculated the destination FLW based on counted crates diverted to loss destinations, using participant-reported destinations (animal feed, field discard), to estimate the postharvest FLW from farm to retail. We used pile sorting with farmers to explore on-farm quality assessments.ResultsThe average preharvest quality FLW was 13.9% of harvested tomatoes. From farm to retail, the quantitative FLW was greatest at the postharvest, farm level. Among all harvests, the median postharvest, farm-level FLW was 0.0% (IQR, 0.0%-7.9%) using the destination FLW approach (tomatoes diverted to nonfood uses) and 2.3% (IQR, 0.0%-12.5%) using the declared FLW approach (P < 0.05). Among harvests with a non-zero postharvest, farm-level FLW, the median FLW was 9.1% (IQR, 2.4%-16.7%) using the destination FLW approach (tomatoes diverted to nonfood uses) and 10.0% (IQR, 2.9%-16.7%) using the declared FLW approach. Harvesting during peak season was a determinant of postharvest, farm-level and preauction, market-level FLW values. Farmers prioritize color/ripeness attributes while harvesting and tomato size while grading.ConclusionsSingle-point estimates may obscure FLW patterns for perishable, indeterminate crops and depend on data collection and estimation methods. Reducing FLW of perishables requires the integration of quantitative and qualitative FLW estimation methods.

Project description:In this paper, we study the topological properties of the global supply chain network in terms of its degree distribution, clustering coefficient, degree-degree correlation, bow-tie structure, and community structure to test the efficient supply chain propositions proposed by E. J.S. Hearnshaw et al. The global supply chain data in the year 2017 are constructed by collecting various company data from the web site of Standard & Poor's Capital IQ platform. The in- and out-degree distributions are characterized by a power law of the form of γin = 2.42 and γout = 2.11. The clustering coefficient decays [Formula: see text] with an exponent βk = 0.46. The nodal degree-degree correlations 〈knn(k)〉 indicates the absence of assortativity. The bow-tie structure of giant weakly connected component (GWCC) reveals that the OUT component is the largest and consists 41.1% of all firms. The giant strong connected component (GSCC) is comprised of 16.4% of all firms. We observe that upstream or downstream firms are located a few steps away from the GSCC. Furthermore, we uncover the community structures of the network and characterize them according to their location and industry classification. We observe that the largest community consists of the consumer discretionary sector based mainly in the United States (US). These firms belong to the OUT component in the bow-tie structure of the global supply chain network. Finally, we confirm the validity of Hearnshaw et al.'s efficient supply chain propositions, namely Proposition S1 (short path length), Proposition S2 (power-law degree distribution), Proposition S3 (high clustering coefficient), Proposition S4 ("fit-gets-richer" growth mechanism), Proposition S5 (truncation of power-law degree distribution), and Proposition S7 (community structure with overlapping boundaries) regarding the global supply chain network. While the original propositions S1 just mentioned a short path length, we found the short path from the GSCC to IN and OUT by analyzing the bow-tie structure. Therefore, the short path length in the bow-tie structure is a conceptual addition to the original propositions of Hearnshaw.

Dataset Information

Intelligent non-colorimetric indicators for the perishable supply chain by non-wovens with photo-programmed thermal response.

Publications

Intelligent non-colorimetric indicators for the perishable supply chain by non-wovens with photo-programmed thermal response.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets