Colm D. Everard

Prediction of moisture, calorific value, ash and carbon content of two dedicated bioenergy crops using near-infrared spectroscopy

The potential of near infrared spectroscopy in conjunction with partial least squares regression to predict Miscanthus xgiganteus and short rotation coppice willow quality indices was examined. Moisture, calorific value, ash and carbon content were predicted with a root mean square error of cross validation of 0.90% (R(2) = 0.99), 0.13 MJ/kg (R(2) = 0.99), 0.42% (R(2) = 0.58), and 0.57% (R(2) = 0.88), respectively. The moisture and calorific value prediction models had excellent accuracy while the carbon and ash models were fair and poor, respectively. The results indicate that near infrared spectroscopy has the potential to predict quality indices of dedicated energy crops, however the models must be further validated on a wider range of samples prior to implementation. The utilization of such models would assist in the optimal use of the feedstock based on its biomass properties.

Effects of cutting intensity and stirring speed on syneresis and curd losses during cheese manufacture.

Recombined whole milk was renneted under constant conditions of pH, temperature, and added calcium, and the gel was cut at a constant firmness. The effects of cutting and stirring on syneresis and curd losses to whey were investigated during cheese making using a factorial design with 3 cutting modes designed to provide 3 different cutting intensity levels (i.e., total cutting revolutions), 3 levels of stirring speed, and 3 replications. These cutting intensities and stirring speeds were selected to give a wide range of curd grain sizes and curd shattering, respectively. Both factors affected curd losses, and correct selection of these factors is important in the cheesemaking industry. Decreased cutting intensity and increased stirring speed significantly increased the losses of fines and fat from the curd to the whey. Cutting intensities and stirring speeds in this study did not show significant effects on curd moisture content over the course of syneresis. Levels of total solids, fines, and fat in whey were shown to change significantly during syneresis. It is believed that larger curd particles resulting from low cutting intensities coupled with faster stirring speeds resulted in a higher degree of curd shattering during stirring, which caused significant curd losses.

Validation of a curd-syneresis sensor over a range of milk composition and process parameters

An online visible-near-infrared sensor was used to monitor the course of syneresis during cheesemaking with the purpose of validating syneresis indices obtained using partial least squares, with cross-validation across a range of milk fat levels, gel firmness levels at cutting, curd cutting programs, stirring speeds, milk protein levels, and fat:protein ratio levels. Three series of trials were carried out in an 11-L cheese vat using recombined whole milk. Three factorial experimental designs were used, consisting of 1) 3 curd stirring speeds and 3 cutting programs; 2) 3 milk fat levels and 3 gel firmness levels at cutting; and 3) 2 milk protein levels and 3 fat:protein ratio levels, respectively. Milk was clotted under constant conditions in all experiments and the gel was cut according to the respective experimental design. Prediction models for production of whey and whey fat losses were developed in 2 of the experiments and validated in the other experiment. The best models gave standard error of prediction values of 6.6 g/100 g for yield of whey and 0.05 g/100 g for fat in whey, as compared with 4.4 and 0.013 g/100 g, respectively, for the calibration data sets. Robust models developed for predicting yield of whey and whey fat losses using a validation method have potential application in the cheese industry.

Prediction of Inorganic Salt and Moisture Content of Process Cheese Using Dielectric Spectroscopy

The development of on-line sensors for compositional analysis during cheese manufacture is desirable for improved quality control. Dielectric properties of a food product are principally determined by its moisture and salt content. This indicates that dielectric spectroscopy may offer a rapid, on-line and non-destructive method for the determination of moisture and salt content of process cheese. However limited information is available in the literature on the dielectric properties of process cheese. Therefore the aims of this study are to investigate the dielectric properties of process cheese samples over a range of compositional parameters and to assess the potential of dielectric spectroscopy to improve process control during process cheese manufacture. Dielectric spectra of process cheese samples were measured using a coaxial line probe between 300 MHz and 3 GHz. A clear tend was observed between higher moisture content and increases in the dielectric constant. Inorganic salt content was found to have a major influence on the loss factor. The dielectric data obtained was used to develop chemometric models for the prediction of moisture and inorganic salt content of two experimental sets of process cheese samples (exp A and exp B). The root mean square error of prediction (RMSEP) for the models developed to predict moisture content were 0.524% (w/w) (exp A), and 0.423% (w/w) (exp B), while the RMSEP of the inorganic salt models were 0.220% (w/w) (exp A), and 0.263% (w/w) (exp B). It was concluded that dielectric spectroscopy has potential application for compositional analysis in process cheese manufacture.

Visible-near infrared spectral sensing coupled with chemometric analysis as a method for on-line prediction of milled biomass composition pre-pelletising.

Chemical composition of biomass is critical to conversion efficiency during pelletisation. Visible-near infrared (vis–NIR) spectral sensing is a rapid and non-destructive sensing technology. The potential of on-line vis–NIR spectral sensing, in conjunction with chemometrics, to predict moisture, carbon and ash contents of milled Miscanthus and two short rotation coppice willow varieties was assessed. Spectroscopic information within the vis–NIR waveband of 400–1000 nm was analysed. Principal component analysis was successfully used to distinguish between the three varieties of biomass. Partial least squares regression validation models for moisture prediction over a range of 1.9–37.0% gave a coefficient of determination (r2) of 0.95 with a root mean square error of prediction of 2.5%. Carbon and ash cross-validation models achieved r2 = 0.85 and 0.50, respectively. These results were for a multiple biomass variety sample set. Results demonstrate on-line vis–NIR spectral sensing combined with chemometrics has the potential to be employed in an integrated pelletising management system.

A Simple Multispectral Imaging Algorithm for Detection of Defects on Red Delicious Apples

Purpose: A multispectral algorithm for detection and differentiation of defective (defects on apple skin) and normal Red Delicious apples was developed from analysis of a series of hyperspectral line-scan images. Methods: A fast line-scan hyperspectral imaging system mounted on a conventional apple sorting machine was used to capture hyperspectral images of apples moving approximately 4 apples per second on a conveyor belt. The detection algorithm included an apple segmentation method and a threshold function, and was developed using three wavebands at 676 nm, 714 nm and 779 nm. The algorithm was executed on line-by-line image analysis, simulating online real-time line-scan imaging inspection during fruit processing. Results: The rapid multispectral algorithm detected over 95% of defectiv e apples and 91% of normal apples investigated. Conclusions: The multispectral defect detection algorithm can potentially be used in commercial apple processing lines.

Effects of milk composition, stir-out time, and pressing duration on curd moisture and yield

A study was undertaken to investigate the effects of milk composition (i.e., protein level and protein:fat ratio), stir-out time, and pressing duration on curd moisture and yield. Milks of varying protein levels and protein:fat ratios were renneted under normal commercial conditions in a pilot-scale cheese vat. During the syneresis phase of cheese making, curd was removed at differing times, and curd moisture and yield were monitored over a 22-h pressing period. Curd moisture after pressing decreased with longer stir-out time and pressing duration, and an interactive effect was observed of stir-out time and pressing duration on curd moisture and yield. Milk total solids were shown to affect curd moisture after pressing, which has implications for milk standardization; that is, it indicates a need to standardize on a milk solids basis as well as on a protein:fat basis. In this study, a decreased protein:fat ratio was associated with increased total solids in milk and resulted in decreased curd moisture and increased curd yield after pressing. The variation in total solids of the milk explains the apparent contradiction between decreased curd moisture and increased curd yield. This study points to a role for process analytic technology in minimizing variation in cheese characteristics through better control of cheesemilk composition, in-vat process monitoring (coagulation and syneresis), and post-vat moisture reduction (curd pressing). Increased control of curd composition at draining would facilitate increased control of the final cheese grade and quality.

Correlation Between Process Cheese Meltability Determined by Sensory Analysis, Computer Vision Method and Olson and Price Test

The meltabilities of 14 process cheese samples were determined at 2 and 4 weeks after manufacture using sensory analysis, a computer vision method, and the Olson and Price test. Sensory analysis meltability correlated with both computer vision meltability (R2 = 0.71, P < 0.001) and Olson and Price meltability (R2 = 0.69, P < 0.001). There was a marked lack of correlation between the computer vision method and the Olson and Price test. This study showed that the Olson and Price test gave greater repeatability than the computer vision method. Results showed process cheese meltability decreased with increasing inorganic salt content and with lower moisture/fat ratios. There was very little evidence in this study to show that process cheese meltability changed between 2 and 4 weeks after manufacture.

Using Light Backscatter at 980 nm to Determine Curd Moisture Content at Various Milk Fat and Gel Firmness Levels in Cheese-making

Curd moisture control plays a fundamental role in determining the quality of the final cheese. A large field of view (LFV) light backscatter sensor at 980 nm was used as a rapid on-line method for monitoring curd moisture content changes during syneresis. Improving the control of curd moisture content during the cheese-making process may enhance product consistency and reduce the overall production cost. The objective of this study was to evaluate the influence of two experimental variables (i.e. fat and gel firmness at cutting) using light backscatter at 980 nm on syneresis and curd moisture content prediction. An experiment was conducted in which whole milk was recombined at three fat levels, clotted under constant conditions and the milk gel was cut at three gel firmness levels. The curd-whey mixture was stirred in the vat after gel cutting and the syneresis process was monitored using the LFV sensor which was installed in the cheese vat wall.

Characterization of Biomass using Hyperspectral Imaging Techniques

Production of sustainable and affordable energy is a major challenge that faces economies worldwide. Increasing the proportion of renewable energies, such as biomass, within our energy supply plays an important role in decreasing greenhouse gasses in the atmosphere. However energy crops and agricultural crop residues are inherently heterogeneous which stresses the need for rapid characterization methods. Development of sensors to predict biomass quality in real time will facilitate optimization of biomass-to-energy conversion processes.