A survey of Canadian dairy nutritionists to assess current trace element formulation practices.

The purpose of this study was to identify current practices and perceptions around trace element feeding for dairy cows through a Canadian dairy nutritionist survey. An online survey with 23 questions was used to collect data from Canadian dairy nutritionists with the help of professional associations and social media. The survey was active from November 2021 to April 2022. The first 7 questions collected descriptive information on respondents, and the subsequent 16 questions focused on trace element feeding. A total of 92 participants from all over Canada filled out the survey, and about 26% of Canadian herds and cows were represented by these respondents. The participants had diverse views on the importance of diet formulations for trace elements to optimize cow health and productivity, with perceptions varying from very important to not important. In comparison, macronutrients and selenium were consistently rated as very important by between 58% and 74% of respondents. Software reference values were used by 54%, 72%, and 73% of participants to estimate trace element concentrations of forages, cereals, and protein sources, respectively, highlighting the importance of regularly updating the feed library of the software. More than 60% of nutritionists participating in this study had intentionally formulated diets above trace element software recommendations, considered mineral interactions occurring in the rumen, and used a trace element source known for its better bioavailability (e.g., organic, chelate) when they formulated diets. Herds with more than 80 cows were more likely to be given trace element supplements known for their greater bioavailability. The most used supplement with enhanced bioavailability was selenium. In addition, different trace element feeding strategies pertaining to different stages of lactation and breeds were reported. This finding can be explained by the absence of clear recommendations on trace element feeding by breed. The participants who adjusted trace element feeding according to the stages of lactation considered the transition period as the most challenging period, and they identified the need for a source of trace element known for its greater bioavailability for this period. Further research should aim to identify environmental risk of trace element overfeeding using the One Health approach. Moreover, strategies to avoid trace element overfeeding should be evaluated.

Internal Quality Attributes and Sensory Characteristics of 'Ambrosia' Apples with Different Dry Matter Content after a Two-Week and a Ten-Week Air Storage at 1 °C.

This research was conducted to determine the compositional and textural characteristics and sensory profile of 'Ambrosia' apples with different dry matter content (DMC) as estimated using a Felix-750 Produce Quality Meter (Felix Instruments Inc., Camas, WA, USA). Fruits were harvested from a commercial orchard in Cawston and an experimental field in Summerland Research and Development Centre (SuRDC) in British Columbia, Canada, when the average absorbance difference index/coefficient of fruit skin δAbsorbance (δA) dropped under 0.45 ± 0.10. DMC levels were estimated after harvest at the blush/background transition zone for fruit categorization on 300 fruits from each location. Fruits were coded with an individual number and grouped in different DMC categories. The distribution of the estimated DMC levels obtained from two locations was different. The results indicate that DMC levels were strongly and positively correlated with the soluble solids content (SSC) of the fruit (r = 0.81). Sensory evaluations also demonstrated that apples in the lowest DMC category (12.5% ± 0.5 from Cawston) were considered the least sweet apples with the least overall flavour quality by panellists compared to the apples from the other DMC categories included in the sensory evaluations from the two locations. Panellists also perceived less-than-expected "fresh apple" and "tropical" flavours but more-than-expected "no flavour" and "bland" off flavour from the lowest-DMC-category apples. The non-destructive DMC measurements show a potential to be used to sort apples for SSC, sweetness and flavour; nevertheless, they were not related to firmness or textural attributes.

Modelling and Classification of Apple Textural Attributes Using Sensory, Instrumental and Compositional Analyses.

Textural characteristics of fruit are important for their quality, storability, and consumer acceptance. While texture can be evaluated instrumentally or sensorially, instrumental measurements are preferred if they can be reliably related to human perception. The objectives of this research were to validate instrumental measurements with sensory determinations, develop a classification scheme to group apples by their textural characteristics, and create models to predict sensory attributes from instrumental and compositional analyses. The textural characteristics (crispness, hardness, juiciness, and skin toughness) of 12 apple cultivars were evaluated on new and established cultivars. Fruit was also evaluated using five instrumental measurements from TA.XTplus Texture Analyzer, and three compositional determinations. The experiment was repeated for analysis and validation purposes. Principal component (PC) analysis revealed that 95.88% of the variation in the instrumental determinations could be explained by two components (PC 1 and PC 2); which were highly correlated with flesh firmness and skin strength, respectively. Four textural groups of apples were identified, and the accuracy of classification was established at 94.44% by using linear discriminant analysis. The predictive models that were developed between the sensory and instrumental-compositional data explained more than 85% of the variation in the data for hardness and crispness, while models for juiciness and skin toughness were more complex. The work should assist industry personnel to reduce time-consuming and costly sensory testing, yet have an appreciation of the textural traits as perceived by the consumer.

Multiple Correspondence and Hierarchical Cluster Analyses for the Profiling of Fresh Apple Customers Using Data from Two Marketplaces.

Purchase behavior and preferences for consumers of fresh apples were investigated using a consumer survey conducted at a special-event apple market. Survey respondents were asked to list apple cultivars they had purchased at the retail market and the special-event market. The special-event market offered many uncommon cultivars packed in clear plastic bags with a fixed weight and price. Respondents were also asked to identify their reasons for selection of each apple cultivar and answer demographic questions. A total of 169 customers completed the survey. Profiles of customers were identified using multiple correspondence analysis (MCA) and hierarchical cluster analysis (HCA), and the impact of the change in available apple cultivars on consumers' purchase behavior was explored. Consumers primarily indicated four main reasons in the selection of their apples: visual appearance, previous experience, taste/aroma, and texture. The first two reasons, evaluated before eating an apple, were loaded on the first MCA dimension, while the last two reasons (i.e., eating quality) were loaded on the second dimension in data from both marketplaces. HCA identified five classes of customers in both markets, and results indicated that similar market segments existed within the two marketplaces, regardless of the availability of apple cultivars.

Inter-correlation of apple firmness determinations and development of cross-validated regression models for prediction of sensory attributes from instrumental and compositional analyses.

The texture of apples is paramount for determining fruit quality. This research explored the correlations among firmness determinations from the Sinclair iQ™ Firmness Tester (SiQ™), the Aweta Acoustic Firmness Sensor (AFS), and eight measurements from the Mohr Digi-Test-2 (MDT) instrument. Assessments were conducted on a collection of nine apple cultivars (Ambrosia, Aurora Golden Gala™, Honeycrisp, Fuji, Imperial Gala, McIntosh, Pink Lady™, Silken, Salish™), with a broad range of firmness values, in each of two years. Sensory analysis of the apples was conducted using a semi-trained panel (n = 10) to evaluate crispness, hardness, juiciness and skin toughness, in quadruplicate at two testing dates, providing eight data points per cultivar per year. Inter-correlations of the instrumental firmness determinations (SiQ™, AFS, MDT) revealed that most values were highly correlated with one another (r > 0.500 n = 72). This suggested that the instruments were tracking similar, but not identical, underlying characteristics. Multiple regression models were developed using the 2016 data to predict the sensory attributes from the instrumental and compositional (titratable acidity, soluble solids concentration, absorbed juice) analyses. Models with the highest R2 were cross-validated using the 2015 data. Accuracy of these models was evaluated using R2 and prediction standard errors (PSEs) - an index quantifying the difference between the predicted and actual values. In general, simple 1- and 2-variable models satisfactorily predicted hardness and crispness, with the R2 values ranging between 85 and 89%, while more complex non-linear models were required to predict juiciness and skin toughness. Correlations coefficients reported in this research allow for interconversion of experimental firmness data, as determined by the SiQ™, AFS and MDT. Regression models predicting hardness, crispness and juiciness from instrumental/compositional analyses, revealed that the quality factor (QF) variable was particularly important for estimation of textural characteristics. Therefore the MDT, among the instruments evaluated, was the instrument of choice for quality assessment of apples. Since cross-validation of the models accounted for a high proportion of the variance (70-82%) in a new data set with small PSEs (2.67-6.36) (on a 100-unit scale), the developed models were appropriate for estimating the apple textural attributes.