Evaluating mid-infrared spectroscopy as a new technique for predicting sensory texture attributes of processed cheese

The objective of this study was to investigate the potential application of mid-infrared spectroscopy for determination of selected sensory attributes in a range of experimentally manufactured processed cheese samples. This study also evaluates mid-infrared spectroscopy against other recently proposed techniques for predicting sensory texture attributes. Processed cheeses (n = 32) of varying compositions were manufactured on a pilot scale. After 2 and 4 wk of storage at 4 degrees C, mid-infrared spectra ( 640 to 4,000 cm(-1)) were recorded and samples were scored on a scale of 0 to 100 for 9 attributes using descriptive sensory analysis. Models were developed by partial least squares regression using raw and pretreated spectra. The mouth-coating and mass-forming models were improved by using a reduced spectral range ( 930 to 1,767 cm(-1)). The remaining attributes were most successfully modeled using a combined range ( 930 to 1,767 cm(-1) and 2,839 to 4,000 cm(-1)). The root mean square errors of cross-validation for the models were 7.4(firmness; range 65.3), 4.6 ( rubbery; range 41.7), 7.1 ( creamy; range 60.9), 5.1(chewy; range 43.3), 5.2(mouth-coating; range 37.4), 5.3 (fragmentable; range 51.0), 7.4 ( melting; range 69.3), and 3.1 (mass-forming; range 23.6). These models had a good practical utility. Model accuracy ranged from approximate quantitative predictions to excellent predictions ( range error ratio = 9.6). In general, the models compared favorably with previously reported instrumental texture models and near-infrared models, although the creamy, chewy, and melting models were slightly weaker than the previously reported near-infrared models. We concluded that mid-infrared spectroscopy could be successfully used for the nondestructive and objective assessment of processed cheese sensory quality..

Prediction of processed cheese instrumental texture and meltability by mid-infrared spectroscopy coupled with chemometric tools

The objective of this study was to determine the potential of mid-infrared spectroscopy coupled with multidimensional statistical analysis for the prediction of processed cheese instrumental texture and meltability attributes. Processed cheeses (n = 32) of varying composition were manufactured in a pilot plant. Following two and four weeks storage at 4 degrees C samples were analysed using texture profile analysis, two meltability tests (computer vision, Olson and Price) and mid-infrared spectroscopy (4000-640 cm(-1)). Partial least squares regression was used to develop predictive models for all measured attributes. Five attributes were successfully modelled with varying degrees of accuracy. The computer vision meltability model allowed for discrimination between high and low melt values (R-2 = 0.64). The hardness and springiness models gave approximate quantitative results (R-2 = 0.77) and the cohesiveness (R-2 = 0.81) and Olson and Price meltability (R-2 = 0.88) models gave good prediction results. (c) 2006 Elsevier Ltd. All rights reserved..

Women, other "fresh” workers, and the new manufacturing workforce of interwar Britain

Structural, organizational, and technological changes in British industry during the interwar years led to a decline in skilled and physically demanding work, while there was a dramatic expansion in unskilled and semiskilled employment. Previous authors have noted that the new un/semiskilled jobs were generally filled by “fresh” workers recruited from outside the core manufacturing workforce, though there is considerable disagreement regarding the composition of this new workforce. This paper examines labour recruitment patterns and strategies using national data and case studies of eight rapidly expanding industrial centres. The new industrial workforce is shown to have been recruited from a “reserve army” of workers with the common features of relative cheapness, flexibility, and weak unionization. These included women, juveniles, local workers in poorly paid nonindustrial sectors, such as agriculture, and (where these other categories were in short supply) relatively young long-distance internal migrants from declining industrial areas.

The Farm-Retail Price Spread in an Imperfectly Competitive Food Industry

Gardner's popular model of perfect competition in the marketing sector is extended to a conjectural-variations oligopoly with endogenous entry. Revising Gardner's comparative statics on the "farm-retail price ratio," tests of hypotheses about food industry conduct are derived. Using data from a recent article by Wohlgenant, which employs Gardner's framework, tests are made of the validity of his maintained hypothesis-that the food industries are perfectly competitive. No evidence is found of departures from competition in the output markets of the food industries of eight commodity groups: (a) beef and veal, (b) pork, (c) poultry, (d) eggs, (e) dairy, (f) processed fruits and vegetables, (g) fresh fruit, and (h) fresh vegetables.

Combined action of S-carvone and mild heat treatment on Listeria monocytogenes Scott A

The combined action of the plant-derived volatile, S-carvone, and mild heat treatment on the food-borne pathogen, Listeria monocytogenes, was evaluated. The viability of exponential phase cultures grown at 8 °C could be reduced by 1·3 log units after exposure to S-carvone (5 mmol l−1) for 30 min at 45 °C, while individual treatment with S-carvone or exposure to 45 °C for 30 min did not result in a loss in viability. Other plant-derived volatiles, namely carvacrol, cinnamaldehyde, thymol and decanal, were also found to reduce the viability of L. monocytogenes in combination with the same mild heat treatment at concentrations of 1·75 mmol l−1, 2·5 mmol l−1, 1·5 mmol l−1 and 2 mmol l−1, respectively. These findings show that essential oil compounds can play an important role in minimally processed foods, and can be used in the concept of Hurdle Technology to reduce the intensity of heat treatment or other individual hurdles.

Systematic study of the impact of fresh water fluxes on the glacial carbon cycle

During glacial periods, atmospheric CO2 concentration increases and decreases by around 15 ppm. At the same time, the climate changes gradually in Antarctica. Such climate changes can be simulated in models when the AMOC (Atlantic Meridional Oceanic Circulation) is weakened by adding fresh water to the North Atlantic. The impact on the carbon cycle is less straightforward, and previous studies give opposite results. Because the models and the fresh water fluxes were different in these studies, it prevents any direct comparison and hinders finding whether the discrepancies arise from using different models or different fresh water fluxes. In this study we use the CLIMBER-2 coupled climate carbon model to explore the impact of different fresh water fluxes. In both preindustrial and glacial states, the addition of fresh water and the resulting slow-down of the AMOC lead to an uptake of carbon by the ocean and a release by the terrestrial biosphere. The duration, shape and amplitude of the fresh water flux all have an impact on the change of atmospheric CO2 because they modulate the change of the AMOC. The maximum CO2 change linearly depends on the time integral of the AMOC change. The different duration, amplitude, and shape of the fresh water flux cannot explain the opposite evolution of ocean and vegetation carbon inventory in different models. The different CO2 evolution thus depends on the AMOC response to the addition of fresh water and the resulting climatic change, which are both model dependent. In CLIMBER-2, the rise of CO2 recorded in ice cores during abrupt events can be simulated under glacial conditions, especially when the sinking of brines in the Southern Ocean is taken into account. The addition of fresh water in the Southern Hemisphere leads to a decline of CO2, contrary to the addition of fresh water in the Northern Hemisphere.

Climatic impacts of fresh water hosing under Last Glacial Maximum conditions: a multi-model study

Fresh water hosing simulations, in which a fresh water flux is imposed in the North Atlantic to force fluctuations of the Atlantic Meridional Overturning Circulation, have been routinely performed, first to study the climatic signature of different states of this circulation, then, under present or future conditions, to investigate the potential impact of a partial melting of the Greenland ice sheet. The most compelling examples of climatic changes potentially related to AMOC abrupt variations, however, are found in high resolution palaeo-records from around the globe for the last glacial period. To study those more specifically, more and more fresh water hosing experiments have been performed under glacial conditions in the recent years. Here we compare an ensemble constituted by 11 such simulations run with 6 different climate models. All simulations follow a slightly different design, but are sufficiently close in their design to be compared. They all study the impact of a fresh water hosing imposed in the extra-tropical North Atlantic. Common features in the model responses to hosing are the cooling over the North Atlantic, extending along the sub-tropical gyre in the tropical North Atlantic, the southward shift of the Atlantic ITCZ and the weakening of the African and Indian monsoons. On the other hand, the expression of the bipolar see-saw, i.e., warming in the Southern Hemisphere, differs from model to model, with some restricting it to the South Atlantic and specific regions of the southern ocean while others simulate a widespread southern ocean warming. The relationships between the features common to most models, i.e., climate changes over the north and tropical Atlantic, African and Asian monsoon regions, are further quantified. These suggest a tight correlation between the temperature and precipitation changes over the extra-tropical North Atlantic, but different pathways for the teleconnections between the AMOC/North Atlantic region and the African and Indian monsoon regions.

Highly efficient neural differentiation of human somatic stem cells, isolated by minimally invasive periodontal surgery

Neural stem cells (NSCs) are potential sources for cell therapy of neurodegenerative diseases and for drug screening. Despite their potential benefits, ethical and practical considerations limit the application of NSCs derived from human embryonic stem cells (ES) or adult brain tissue. Thus, alternative sources are required to satisfy the criteria of ready accessibility, rapid expansion in chemically defined media and reliable induction to a neuronal fate. We isolated somatic stem cells from the human periodontium that were collected during minimally invasive periodontal access flap surgery as part of guided tissue regeneration therapy. These cells could be propagated as neurospheres in serum-free medium, which underscores their cranial neural crest cell origin. Culture in the presence of epidermal growth factor (EGF) and fibroblast growth factor-2 (FGF-2) under serum-free conditions resulted in large numbers of nestin-positive/Sox-2-positive NSCs. These periodontium-derived (pd) NSCs are highly proliferative and migrate in response to chemokines that have been described as inducing NSC migration. We used immunocytochemical techniques and RT-PCR analysis to assess neural differentiation after treatment of the expanded cells with a novel induction medium. Adherence to substrate, growth factor deprivation, and retinoic acid treatment led to the acquisition of neuronal morphology and stable expression of markers of neuronal differentiation by more than 90% of the cells. Thus, our novel method might provide nearly limitless numbers of neuronal precursors from a readily accessible autologous adult human source, which could be used as a platform for further experimental studies and has potential therapeutic implications.

Consumers perception and evaluation of fresh cut buying attributes: a survey on the Italian market

During the last twenty years, consumer choice in high income countries is no longer merely dictated by price and the organoleptic characteristics of a product, but also by other features some of which are not patently tangible. The growing importance of such attributes in the process of consumer choice is not only due to income increase, but also to changes in lifestyle such as migrations from the countryside, a generalized urbanization and consequential city life style, female emancipation and work outside the domestic walls for women, the drastic decrease in hard physical labor and the process of internationalization. The present survey study aims to explore the importance that Italian consumers give to fresh cut buying attributes and which of these attributes should be taken into consideration by industries in order to satisfy the needs of the most critical shoppers. Where possible, market and survey data for fresh cut products will be compared with those for cooked products and before presenting the results and conclusions of the study, the technical issues of processing will be highlighted owing to the fact that they affect the marketing of these products, the recent market situation with regard to consumption will be illustrated and the methodology used will be described.

Prediction of metabolisable energy concentrations of fresh cut grass using digestibility data measured with non-pregnant non-lactating cows

Pasture-based ruminant production systems are common in certain areas of the world, but energy evaluation in grazing cattle is performed with equations developed, in their majority, with sheep or cattle fed total mixed rations. The aim of the current study was to develop predictions of metabolisable energy (ME) concentrations in fresh-cut grass offered to non-pregnant non-lactating cows at maintenance energy level, which may be more suitable for grazing cattle. Data were collected from three digestibility trials performed over consecutive grazing seasons. In order to cover a range of commercial conditions and data availability in pasture-based systems, thirty-eight equations for the prediction of energy concentrations and ratios were developed. An internal validation was performed for all equations and also for existing predictions of grass ME. Prediction error for ME using nutrient digestibility was lowest when gross energy (GE) or organic matter digestibilities were used as sole predictors, while the addition of grass nutrient contents reduced the difference between predicted and actual values, and explained more variation. Addition of N, GE and diethyl ether extract (EE) contents improved accuracy when digestible organic matter in DM was the primary predictor. When digestible energy was the primary explanatory variable, prediction error was relatively low, but addition of water-soluble carbohydrates, EE and acid-detergent fibre contents of grass decreased prediction error. Equations developed in the current study showed lower prediction errors when compared with those of existing equations, and may thus allow for an improved prediction of ME in practice, which is critical for the sustainability of pasture-based systems.

Prediction of nutrient digestibility and energy concentrations in fresh grass using nutrient composition

Improved nutrient utilization efficiency is strongly related to enhanced economic performance and reduced environmental footprint of dairy farms. Pasture-based systems are widely used for dairy production in certain areas of the world, but prediction equations of fresh grass nutritive value (nutrient digestibility and energy concentrations) are limited. Equations to predict digestible energy (DE) and metabolizable energy (ME) used for grazing cattle have been either developed with cattle fed conserved forage and concentrate diets or sheep fed previously frozen grass, and the majority of them require measurements less commonly available to producers, such as nutrient digestibility. The aim of the present study was therefore to develop prediction equations more suitable to grazing cattle for nutrient digestibility and energy concentrations, which are routinely available at farm level by using grass nutrient contents as predictors. A study with 33 nonpregnant, nonlactating cows fed solely fresh-cut grass at maintenance energy level for 50 wk was carried out over 3 consecutive grazing seasons. Freshly harvested grass of 3 cuts (primary growth and first and second regrowth), 9 fertilizer input levels, and contrasting stage of maturity (3 to 9 wk after harvest) was used, thus ensuring a wide representation of nutritional quality. As a result, a large variation existed in digestibility of dry matter (0.642-0.900) and digestible organic matter in dry matter (0.636-0.851) and in concentrations of DE (11.8-16.7 MJ/kg of dry matter) and ME (9.0-14.1 MJ/kg of dry matter). Nutrient digestibilities and DE and ME concentrations were negatively related to grass neutral detergent fiber (NDF) and acid detergent fiber (ADF) contents but positively related to nitrogen (N), gross energy, and ether extract (EE) contents. For each predicted variable (nutrient digestibilities or energy concentrations), different combinations of predictors (grass chemical composition) were found to be significant and increase the explained variation. For example, relatively higher R(2) values were found for prediction of N digestibility using N and EE as predictors; gross-energy digestibility using EE, NDF, ADF, and ash; NDF, ADF, and organic matter digestibilities using N, water-soluble carbohydrates, EE, and NDF; digestible organic matter in dry matter using water-soluble carbohydrates, EE, NDF, and ADF; DE concentration using gross energy, EE, NDF, ADF, and ash; and ME concentration using N, EE, ADF, and ash. Equations presented may allow a relatively quick and easy prediction of grass quality and, hence, better grazing utilization on commercial and research farms, where nutrient composition falls within the range assessed in the current study.

Evaluating nitrogen utilization efficiency of nonpregnant dry cows offered solely fresh cut grass at maintenance levels

The present study aimed to identify key parameters influencing N utilization and develop prediction equations for manure N output (MN), feces N output (FN), and urine N output (UN). Data were obtained under a series of digestibility trials with nonpregnant dry cows fed fresh grass at maintenance level. Grass was cut from 8 different ryegrass swards measured from early to late maturity in 2007 and 2008 (2 primary growth, 3 first regrowth, and 3 second regrowth) and from 2 primary growth early maturity swards in 2009. Each grass was offered to a group of 4 cows and 2 groups were used in each of the 8 swards in 2007 and 2008 for daily measurements over 6 wk; the first group (first 3 wk) and the second group (last 3 wk) assessed early and late maturity grass, respectively. Average values of continuous 3-d data of N intake (NI) and output for individual cows ( = 464) and grass nutrient contents ( = 116) were used in the statistical analysis. Grass N content was positively related to GE and ME contents but negatively related to grass water-soluble carbohydrates (WSC), NDF, and ADF contents ( < 0.01), indicating that accounting for nutrient interrelations is a crucial aspect of N mitigation. Significantly greater ratios of UN:FN, UN:MN, and UN:NI were found with increased grass WSC contents and ratios of N:WSC, N:digestible OM in total DM (DOMD), and N:ME ( < 0.01). Greater NI, animal BW, and grass N contents and lower grass WSC, NDF, ADF, DOMD, and ME concentrations were significantly associated with greater MN, FN, and UN ( < 0.05). The present study highlighted that using grass lower in N and greater in fermentable energy in animals fed solely fresh grass at maintenance level can improve N utilization, reduce N outputs, and shift part of N excretion toward feces rather than urine. These outcomes are highly desirable in mitigation strategies to reduce nitrous oxide emissions from livestock. Equations predicting N output from BW and grass N content explained a similar amount of variability as using NI and grass chemical composition (excluding DOMD and ME), implying that parameters easily measurable in practice could be used for estimating N outputs. In a research environment, where grass DOMD and ME are likely to be available, their use to predict N outputs is highly recommended because they strongly improved of the equations in the current study.