Development of innovative, quick-cook legume products: an investigation of the soaking, cooking and dehydration characteristics of chickpeas (Cicer arietinum L.) and soybeans (Glycine max L. Merr.)

The primary goal of this research was to create new alternatives to the legume products currently available to consumers, i.e. canned and dry beans. Chickpeas and soybeans are well established in the Irish consumer market and possess excellent nutritional quality, such as high protein, fibre and phytochemcial content, low cholesterol and low glycaemic index (G.I.), and therefore have potential for classification as functional foods. The first stage of the research culminated with the development of quick-cook chickpeas and soybeans that could be stored in the chill cabinet or freezer. Water intake and textural attributes during soaking were investigated. Using non-linear regression and analysis, asymptotic models were constructed to predict hydration characteristics as functions of soaking time, temperature and blanching pre-treatment. Optimal cooking treatment was estimated by investigating the effect of boiling and microwave processing on texture and sensory characteristics. Shelf life was estimated for pre-cooked samples under chilled, frozen and freeze-chill storage and it was shown that these products could be kept in chilled storage for up to two weeks and in frozen or freeze-chill storage for up to 12 months. In the second stage of research, shelf-stable, dehydrated, quick-cook chickpeas and soybeans were developed. The application of combined microwave-convective drying to pre-cooked chickpeas and soybeans was investigated on a pilot scale. Dehydration kinetics were fitted to an nth order asymptotic model, known as the Page model and rehydration kinetics were fitted to an asymptotic model. Water activity of soybeans and chickpeas was lowered during drying to a value of 0.35, so that the dehydrated products could potentially be stored at room temperature for up to 12 months

The hype in spectral imaging

Hyperspectral imaging is currently a very well-known and much used technology for measuring features in different fields, such as chemistry, geology, medicine, food and agriculture, either spaceborne (satellites), airborne (drones) or at close proximity (e.g. field scanning, industrial sorting lines or microscopy). Its background is two-fold, and it can be considered as a special case of spectroscopy (“imaging spectroscopy”) or a special case of imaging (“spectral imaging”). Current practice is to use adjectives such as multi and hyper added to “spectral imaging” in order to characterise the number of wavelength bands. In this paper we propose the community to use scientifically sound terminology, like “imaging spectroscopy” or “spectral imaging”, without using ambiguous adjectives. Further, we encourage the community to define and agree upon clear adjectives to describe the number of variables in the naming of our imaging technique

Classification of hardened cement and lime mortar using short-wave infrared spectrometry data

This paper evaluated the feasibility of using spectrometry data in the short-wave infrared range (1,300-2,200nm) to distinguish lime mortar and Type S cement mortar using 42 lab samples (21 lime-based, 21 cement-based) each 404040mm were created. A Partial Least Squares Discriminant Analysis model was developed using the mean spectra of 28 specimens as the calibration set. The results were tested on the mean spectra of the remaining 14 specimens as a validation set. The results showed that, spectrometry data were able to fully distinguish modern mortars (made with cement) from historic lime mortars with a 100% classification accuracy, which can be very useful in archaeological and architectural conservation applications. Specifically, being able to distinguish mortar composition in situ can provide critical information about the construction history of a structure, as well as to inform an appropriate intervention scheme when historic material needs to be repaired or replaced.Funding for this work was provided by New York University’s Center for Urban Science and Progress. Dr. Gowen acknowledges funding from the European Research Council (ERC) under the starting grant programme ERC-2013-StG call—Proposal No. 335508—BioWater

Visible Near-Infrared Hyperspectral Imaging for the Identification and Discrimination of Brown Blotch Disease on Mushroom (Agaricus bisporus) Caps

Brown blotch, caused by pathogenic Pseudomonas tolaasii (P. tolaasii), is the most problematic bacterial disease in Agaricus bisporus mushrooms. Although it does not cause any health problems, it reduces the consumer appeal of mushrooms in the market place, generating important economical losses worldwide. Hyperspectral imaging (HSI) is a non-destructive technique that combines imaging and spectroscopy to obtain information from a sample. The objective of this study was to investigate the use of HSI for brown blotch identification and discrimination from mechanical damage on mushrooms. Hyperspectral images of mushrooms subjected to i) no treatment, ii) mechanical damage or iii) microbiological spoilage were taken during storage and spectra representing each of the classes were selected. Partial least squares- discriminant analysis (PLS-DA) was carried out in two steps: i) discrimination between undamaged and damaged mushrooms and ii) discrimination between damage sources (i.e. mechanical or microbiological). The models were applied at a pixel level and a decision tree was used to classify mushrooms into one of the aforementioned classes. A correct classification of \u3e95% was achieved. Results from this study could be used for the development of a sensor to detect and classify mushroom damage of mechanical and microbial origin, which would facilitate the industry to make rapid and automated decisions to discard produce of poor marketability

Prediction of Polyphenol Oxidase Activity Using Visible Near-Infrared Hyperspectral Imaging on Mushroom (Agaricus bisporus) Caps.

Physical stress (i.e. bruising) during harvesting, handling and transportation triggers enzymatic discoloration of mushrooms, a common and detrimental phenomenon largely mediated by polyphenol oxidase (PPO) enzymes. Hyperspectral imaging (HSI) is a non-destructive technique that combines imaging and spectroscopy to obtain information from a sample. The objective of this study was to assess the ability of HSI to predict the activity of PPO on mushroom caps. Hyperspectral images of mushrooms subjected to various damage treatments were taken, followed by enzyme extraction and PPO activity measurement. Principal component regression (PCR) models (each with 3 PCs) built on raw reflectance and multiple scatter corrected (MSC) reflectance data were found to be the best modeling approach. Prediction maps showed that the MSC model allowed for compensation of spectral differences due to sample curvature and surface irregularities. Results reveal the possibility of developing a sensor which could rapidly identify mushrooms with higher likelihood to develop enzymatic browning and hence aid produce management decision makers in the industry

Comparison of spectral selection methods in the development of classification models from visible near infrared hyperspectral imaging data

Applications of hyperspectral imaging (HSI) to the quantitative and qualitative measurement of samples have grown widely in recent years, due mainly to the improved performance and lower cost of imaging spectroscopy instrumentation. Data sampling is a crucial yet often overlooked step in hyperspectral image analysis, which impacts the subsequent results and their interpretation. In the selection of pixel spectra for the calibration of classification models, the spatial information in HSI data can be exploited. In this paper, a variety of sampling strategies for selection of pixel spectra are presented, exemplified through five case studies. The strategies are compared in terms of the proportion of global variability captured, practicality and predictive model performance. The use of variographic analysis as a guide to the spatial segmentation prior to sampling leads to the selection of representative subsets while reducing the variation in model performance parameters over repeated random selection

Cold Atmospheric Plasma Induces Silver Nanoparticle Uptake, Oxidative Dissolution and Enhanced Cytotoxicity in Glioblastoma Multiforme Cells

Silver nanoparticles (AgNP) emerged as a promising reagent for cancer therapy with oxidative stress implicated in the toxicity. Meanwhile, studies reported cold atmospheric plasma (CAP) generation of reactive oxygen and nitrogen species has selectivity towards cancer cells. Gold nanoparticles display synergistic cytotoxicity when combined with CAP against cancer cells but there is a paucity of information using AgNP, prompting to investigate the combined effects of CAP using dielectric barrier discharge system (voltage of 75 kV, current is 62.5 mA, duty cycle of 7.5kVA and input frequency of 50–60Hz) and 10 nm PVA-coated AgNP using U373MG Glioblastoma Multiforme cells. Cytotoxicity in U373MG cells was \u3e100-fold greater when treated with both CAP and PVA-AgNP compared with either therapy alone (IC50 of 4.30 μg/mL with PVA-AgNP alone compared with 0.07 μg/mL after 25s CAP and 0.01 μg/mL 40s CAP). Combined cytotoxicity was ROS-dependent and was prevented using N-Acetyl Cysteine. A novel darkfield spectral imaging method investigated and quantified AgNP uptake in cells determining significantly enhanced uptake, aggregation and subcellular accumulation following CAP treatment, which was confirmed and quantified using atomic absorption spectroscopy. The results indicate that CAP decreases nanoparticle size, decreases surface charge distribution of AgNP and induces uptake, aggregation and enhanced cytotoxicity in vitro

Feature Fusion of Raman Chemical Imaging and Digital Histopathology using Machine Learning for Prostate Cancer Detection

The diagnosis of prostate cancer is challenging due to the heterogeneity of its presentations, leading to the over diagnosis and treatment of non-clinically important disease. Accurate diagnosis can directly benefit a patient's quality of life and prognosis. Towards addressing this issue, we present a learning model for the automatic identification of prostate cancer. While many prostate cancer studies have adopted Raman spectroscopy approaches, none have utilised the combination of Raman Chemical Imaging (RCI) and other imaging modalities. This study uses multimodal images formed from stained Digital Histopathology (DP) and unstained RCI. The approach was developed and tested on a set of 178 clinical samples from 32 patients, containing a range of non-cancerous, Gleason grade 3 (G3) and grade 4 (G4) tissue microarray samples. For each histological sample, there is a pathologist labelled DP - RCI image pair. The hypothesis tested was whether multimodal image models can outperform single modality baseline models in terms of diagnostic accuracy. Binary non-cancer/cancer models and the more challenging G3/G4 differentiation were investigated. Regarding G3/G4 classification, the multimodal approach achieved a sensitivity of 73.8% and specificity of 88.1% while the baseline DP model showed a sensitivity and specificity of 54.1% and 84.7% respectively. The multimodal approach demonstrated a statistically significant 12.7% AUC advantage over the baseline with a value of 85.8% compared to 73.1%, also outperforming models based solely on RCI and median Raman spectra. Feature fusion of DP and RCI does not improve the more trivial task of tumour identification but does deliver an observed advantage in G3/G4 discrimination. Building on these promising findings, future work could include the acquisition of larger datasets for enhanced model generalization.Comment: 19 pages, 8 tables, 18 figure

Feature Fusion of Raman Chemical Imaging and Digital Histopathology using Machine Learning for Prostate Cancer Detection

The diagnosis of prostate cancer is challenging due to the heterogeneity of its presentations, leading to the over diagnosis and treatment of non-clinically important disease. Accurate diagnosis can directly benefit a patient’s quality of life and prognosis. Towards addressing this issue, we present a learning model for the automatic identification of prostate cancer. While many prostate cancer studies have adopted Raman spectroscopy approaches, none have utilised the combination of Raman Chemical Imaging (RCI) and other imaging modalities. This study uses multimodal images formed from stained Digital Histopathology (DP) and unstained RCI. The approach was developed and tested on a set of 178 clinical samples from 32 patients, containing a range of non-cancerous, Gleason grade 3 (G3) and grade 4 (G4) tissue microarray samples. For each histological sample, there is a pathologist labelled DP - RCI image pair. The hypothesis tested was whether multimodal image models can outperform single modality baseline models in terms of diagnostic accuracy. Binary non-cancer/cancer models and the more challenging G3/G4 differentiation were investigated. Regarding G3/G4 classification, the multimodal approach achieved a sensitivity of 73.8% and specificity of 88.1% while the baseline DP model showed a sensitivity and specificity of 54.1% and 84.7% respectively. The multimodal approach demonstrated a statistically significant 12.7% AUC advantage over the baseline with a value of 85.8% compared to 73.1%, also outperforming models based solely on RCI and median Raman spectra. Feature fusion of DP and RCI does not improve the more trivial task of tumour identification but does deliver an observed advantage in G3/G4 discrimination. Building on these promising findings, future work could include the acquisition of larger datasets for enhanced model generalization