NIR Hyperspectral Imaging for Mapping of Moisture Content Distribution in Tea Buds during Dehydration

This work employed hyperspectral imaging technique to map the spatial distribution of moisture content (MC) in tea buds during dehydration. Hyperspectral images (874–1734 nm) of tea buds were acquired in six dehydrated periods (0, 3, 6, 9, 14 and 21 min) at 80°C. The spectral reflectance of tea buds were extracted from region of interests (ROIs) in the hyperspectral images. Competitive adaptive reweighted sampling (CARS) was used to select effective wavelengths (EWs) and ten representing the wavelengths were selected. The quantitative relationship between spectral reflectance and the measured MC values of tea buds was built using partial least square regression (PLSR) based on full spectra and EWs. The quantitative model established using EWs, which had a result of coefficient of correlation (RP) of 0.941 and root mean square error of prediction (RMSEP) of 5.31%, was considered as the optimal model for mapping MC distribution. The optimal model was finally applied to predict the MC of each pixel within of the tea bud sample and built the MC distribution maps by utilization of a developed image processing procedure. Results demonstrated that the hyperspectral imaging technique has the potential of mapping the MC spatial distribution in tea buds in dehydrated process

A review of optical nondestructive visual and near-infrared methods for food quality and safety

This paper is a review of optical methods for online nondestructive food quality monitoring. The key spectral areas are the visual and near-infrared wavelengths. We have collected the information of over 260 papers published mainly during the last 20 years. Many of them use an analysis method called chemometrics which is shortly described in the paper. The main goal of this paper is to provide a general view of work done according to different FAO food classes. Hopefully using optical VIS/NIR spectroscopy gives an idea of how to better meet market and consumer needs for high-quality food stuff.©2013 the Authors. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.fi=vertaisarvioitu|en=peerReviewed

Development of innovative analytical methods based on spectroscopic techniques and multivariate statistical analysis for quality control in the food and pharmaceutical fields.

The increasing demand on quality assurance and ever more stringent regulations in food and pharmaceutical fields are promoting the need for analytical techniques enabling to provide reliable and accurate results. However, traditional analytical methods are labor-intensive, time-consuming, expensive and they usually require skilled personnel for performing the analysis. For these reasons, in the last decades, quality control protocols based on the employment of spectroscopic methods have been developed for many different application fields, including pharmaceutical and food ones. Vibrational spectroscopic techniques can be an adequate alternative for acquiring both chemical and physical information related to homogenous and heterogenous matrices of interest. Moreover, the significant development of powerful data-driven methodologies allowed to develop algorithms for the optimal extraction and processing of the complex spectroscopic signals allowing to apply combined approaches for quantitative and qualitative purposes. The present Doctoral Thesis has been focused on the development of ad-hoc analytical strategies based on the application of spectroscopic techniques coupled with multivariate data analysis approaches for providing alternative analytical protocols for quality control in food and pharmaceutical sectors. Regarding applications in food sector, excitation-emission Fluorescence Spectroscopy, Near Infrared Spectroscopy (NIRS) and NIR Hyperspectral Imaging (HSI) have been tested for solving analytical issues of independent case-studies. Unsupervised approaches based on Principal Component Analysis (PCA) and Parallel Factor Analysis (PARAFAC) have been applied on fluorescence data for characterizing green tea samples, while quantitative predictive approaches as Partial Least Squares regression have been used to correlate NIR spectra with quality parameters of extra-virgin olive oil samples. HSI was applied to study dynamic chemical processes which occur during cheese ripening with the aim to map chemical and sensory changes over time. The rapid technical progress in terms of spectroscopic instrumentations has led to have more flexible portable systems suitable for performing measurements directly in the field or in a manufacturing plant. Within this scenario, NIR spectroscopy proved to be one of the most powerful Process Analytical Technologies (PAT) for monitoring and controlling complex manufacturing processes. In this thesis, two applications based on the implementation of miniaturized NIR sensors have been performed for the real-time powder blending monitoring of pharmaceutical and food formulation, respectively. The main challenges in blending monitoring are related to the assessment of the homogeneity of multicomponent formulations, which is crucial to ensure the safety and effectiveness of a solid pharmaceutical formulation or the quality of a food product. In the third chapter of this thesis, tailor made qualitative chemometric strategies for obtaining a global understanding of blending processes and to optimize the endpoint detection are presented

Simulation of site-specific irrigation control strategies with sparse input data

Crop and irrigation water use efficiencies may be improved by managing irrigation application timing and volumes using physical and agronomic principles. However, the crop water requirement may be spatially variable due to different soil properties and genetic variations in the crop across the field. Adaptive control strategies can be used to locally control water applications in response to in-field temporal and spatial variability with the aim of maximising both crop development and water use efficiency. A simulation framework ‘VARIwise’ has been created to aid the development, evaluation and management of spatially and temporally varied adaptive irrigation control strategies (McCarthy et al., 2010). VARIwise enables alternative control strategies to be simulated with different crop and environmental conditions and at a range of spatial resolutions. An iterative learning controller and model predictive controller have been implemented in VARIwise to improve the irrigation of cotton. The iterative learning control strategy involves using the soil moisture response to the previous irrigation volume to adjust the applied irrigation volume applied at the next irrigation event. For field implementation this controller has low data requirements as only soil moisture data is required after each irrigation event. In contrast, a model predictive controller has high data requirements as measured soil and plant data are required at a high spatial resolution in a field implementation. Model predictive control involves using a calibrated model to determine the irrigation application and/or timing which results in the highest predicted yield or water use efficiency. The implementation of these strategies is described and a case study is presented to demonstrate the operation of the strategies with various levels of data availability. It is concluded that in situations of sparse data, the iterative learning controller performs significantly better than a model predictive controller

Air pollution and livestock production

The air in a livestock farming environment contains high concentrations of dust particles and gaseous pollutants. The total inhalable dust can enter the nose and mouth during normal breathing and the thoracic dust can reach into the lungs. However, it is the respirable dust particles that can penetrate further into the gas-exchange region, making it the most hazardous dust component. Prolonged exposure to high concentrations of dust particles can lead to respiratory health issues for both livestock and farming staff. Ammonia, an example of a gaseous pollutant, is derived from the decomposition of nitrous compounds. Increased exposure to ammonia may also have an effect on the health of humans and livestock. There are a number of technologies available to ensure exposure to these pollutants is minimised. Through proactive means, (the optimal design and management of livestock buildings) air quality can be improved to reduce the likelihood of risks associated with sub-optimal air quality. Once air problems have taken hold, other reduction methods need to be applied utilising a more reactive approach. A key requirement for the control of concentration and exposure of airborne pollutants to an acceptable level is to be able to conduct real-time measurements of these pollutants. This paper provides a review of airborne pollution including methods to both measure and control the concentration of pollutants in livestock buildings

Scale-Wavelength Decomposition of Hyperspectral Signals - Use for Mineral Classification & Quantification

An approach for material identification & soil constituent quantification based on a generalized multi-scale derivative analysis of hyperspectral signals is presented. It employs the continuous wavelet transform to project input spectra onto a scale-wavelength space. This allows investigating the spectra at selectable level of detail while normalizing/separating disturbances. Benefits & challenges of this decomposition for mineral classification & quantification will be shown for a mining site

Improving ecological forecasts using model and data constraints

Terrestrial ecosystems are essential to human well-being, but their future remains highly uncertain, as evidenced by the huge disparities in model projections of the land carbon sink. The existence of these disparities despite the recent explosion of novel data streams, including the TRY plant traits database, the Landsat archive, and global eddy covariance tower networks, suggests that these data streams are not being utilized to their full potential by the terrestrial ecosystem modeling community. Therefore, the overarching objective of my dissertation is to identify how these various data streams can be used to improve the precision of model predictions by constraining model parameters. In chapter 1, I use a hierarchical multivariate meta-analysis of the TRY database to assess the dependence of trait correlations on ecological scale and evaluate the utility of these correlations for constraining ecosystem model parameters. I find that global trait correlations are generally consistent within plant functional types, and leveraging the multivariate trait space is an effective way to constrain trait estimates for data-limited traits and plant functional types. My next two chapters assess the ability to measure traits using remote sensing by exploring the links between leaf traits and reflectance spectra. In chapter 2, I introduce a method for estimating traits from spectra via radiative transfer model inversion. I then use this approach to show that although the precise location, width, and quantity of spectral bands significantly affects trait retrieval accuracy, a wide range of sensor configurations are capable of providing trait information. In chapter 3, I apply this approach to a large database of leaf spectra to show that traits vary as much within as across species, and much more across species within a functional type than across functional types. Finally, in chapter 4, I synthesize the findings of the previous chapters to calibrate a vegetation model's representation of canopy radiative transfer against observed remotely-sensed surface reflectance. Although the calibration successfully constrained canopy structural parameters, I identify issues with model representations of wood and soil reflectance that inhibit its ability to accurately reproduce remote sensing observations