PurposeThe purpose of this paper is to (1) investigate the effect of freshness on consumers' willingness to pay, (2) derive static and dynamic pricing strategies and (3) compare the effect of these pricing strategies on a retailer's revenue and food waste. This investigation helps to reveal the potentials of dynamic pricing strategies for building more sustainable business models.Design/methodology/approachThe authors conduct an online experiment to measure consumers' willingness to pay for fresh and three-days’ old strawberries. The impact of freshness on willingness to pay is analysed using univariate tests and regression analysis. Pricing strategies are compared using a Monte Carlo simulation.FindingsThe results of this study show that freshness largely determines consumers' willingness to pay and price sensitivity. This renders dynamic pricing a promising strategy from an economic point of view. The results of the simulation study show that food waste can be reduced by up to 53.6% with a dynamic pricing instead of a static pricing strategy in the case that there are as many consumers as strawberry packages in the inventory. Revenue can be increased by up to 10% compared to a static pricing strategy based on fresh strawberries.Practical implicationsThis study suggests that food retailers can improve their revenue when switching from static to dynamic pricing. Furthermore, in most cases, food retailers can reduce food waste with a dynamic instead of a static-pricing strategy, which might help to improve their image through a more sustainable business model and attract additional consumers.Originality/valueThis study is the first to analyse the possibility of using food freshness to design a dynamic pricing strategy and to analyse the impact of such a pricing strategy on both, a retailer's revenue and a retailer's food waste.

Spectrometers measure diffuse reflectance and create a “molecular fingerprint” of the material under investigation. Ruggedized, small scale devices for “in-field” use cases exist. Such devices might for example be used by companies in the food supply chain for inward inspection of goods. However, their application for the industrial Internet of Things workflows or scientific research is limited due to their proprietary nature. We propose an open platform for visible and near-infrared technology (OpenVNT), an open platform for capturing, transmitting, and analysing spectral measurements. It is built for use in the field, as it is battery-powered and transmits data wireless. To achieve high accuracy, the OpenVNT instrument contains two spectrometers covering a wavelength range of 400–1700 nm. We conducted a study on white grapes to compare the performance of the OpenVNT instrument against the Felix Instruments F750, an established commercial instrument. Using a refractometer as ground truth, we built and validated models to estimate the Brix value. As a quality measure, we used coefficient of determination of the cross-validation (R2CV) between the instrument estimation and ground truth. With 0.94 for the OpenVNT and 0.97 for the F750, a comparable R2CV was achieved for both instruments. OpenVNT matches the performance of commercially available instruments at one tenth of the price. We provide an open bill of materials, building instructions, firmware, and analysis software to enable research and industrial IOT solutions without the limitations of walled garden platforms.

Typical electroless copper baths (ECBs), which are used to chemically deposit copper on printed circuit boards, consist of an aqueous alkali hydroxide solution, a copper(II) salt, formaldehyde as reducing agent, an L-(+)-tartrate as complexing agent, and a 2,2′-bipyridine derivative as stabilizer. Actual speciation and reactivity are, however, largely unknown. Herein, we report on the synthesis and crystal structure of aqua-1κO-bis(4,4′-dimethoxy-2,2′-bipyridine)-1κ2 N,N′;2κ2 N,N′-[μ-(2R,3R)-2,3-dioxidosuccinato-1κ2 O 1,O 2:2κ2 O 3,O 4]dicopper(II) octahydrate, [Cu2(C12H12N2O2)2(C4H2O6)(H2O)]·8H2O, from an ECB mock-up. The title compound crystallizes in the Sohncke group P21 with one chiral dinuclear complex and eight molecules of hydrate water in the asymmetric unit. The expected retention of the tartrato ligand's absolute configuration was confirmed via determination of the absolute structure. The complex molecules exhibit an ansa-like structure with two planar, nearly parallel bipyridine ligands, each bound to a copper atom that is connected to the other by a bridging tartrato `handle'. The complex and water molecules give rise to a layered supramolecular structure dominated by alternating π stacks and hydrogen bonds. The understanding of structures ex situ is a first step on the way to prolonged stability and improved coating behavior of ECBs.

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Chemically pure plastic granulate is used as the starting material in the production of plastic parts. Extrusion machines rely on purity, otherwise resources are lost, and waste is produced. To avoid losses, the machines need to analyze the raw material. Spectroscopy in the visible and near-infrared range and machine learning can be used as analyzers. We present an approach using two spectrometers with a spectral range of 400–1700 nm and a fusion model comprising classification, regression, and validation to detect 25 materials and proportions of their binary mixtures. one dimensional convolutional neural network is used for classification and partial least squares regression for the estimation of proportions. The classification is validated by reconstructing the sample spectrum using the component spectra in linear least squares fitting. To save time and effort, the fusion model is trained on semi-empirical spectral data. The component spectra are acquired empirically and the binary mixture spectra are computed as linear combinations. The fusion model achieves very a high accuracy on visible and near-infrared spectral data. Even in a smaller spectral range from 400–1100 nm, the accuracy is high. The visible and near-infrared spectroscopy and the presented fusion model can be used as a concept for building an analyzer. Inexpensive silicon sensor-based spectrometers can be used.