Raw milk microbiota is influenced by farming practices, environmental exposure, and seasonal changes. This study investigated how organic and conventional dairy farming practices influence the microbial composition of raw milk over a 1-yr period. Milk and environmental samples were collected quarterly from 7 dairy farms (4 organic and 3 conventional) in Oregon and analyzed using microbiome sequencing. Across all seasons, the microbial community of raw milk was largely similar between organic and conventional farms, with Escherichia-Shigella being the most abundant genus. Aerobic plate counts were significantly higher in conventional raw milk during winter and summer. Organic milk showed greater seasonal variation in α diversity (Shannon index 1.81 ± 0.40 in winter to 1.01 ± 0.34 in fall), whereas conventional milk remained more stable. No significant β diversity differences were observed between farming types. Shared microbial taxa between raw milk and environmental sources varied by season and farming type, reflecting the influence of confinement and grazing. For example, Romboutsia was more abundant during grazing seasons in organic farms, whereas Clostridium sensu stricto 1 appeared uniquely in conventional milk in winter. These seasonal and housing-related trends highlight how farm management shapes milk microbiota.

The rising popularity of raw goat milk has heightened concerns about its safety. This study examined how differences in milking and cleaning practices influence the quality and microbiota of goat milk from small-scale Oregon farms during July and August. Milk quality was assessed through somatic cell counts (SCCs) and components, while microbiota was evaluated using viable counts and 16S rRNA sequencing. Sequencing revealed a diverse microbial community, dominated by genera such as Staphylococcus, Escherichia-Shigella, and Pseudomonas, with pathogenic taxa like Salmonella and Campylobacter largely absent or detected at negligible levels. Alpha diversity varied significantly among sample types but not across farms, and beta diversity indicated considerable dissimilarity in microbial composition. Importantly, regression models identified significant associations between hygiene practices and bacterial abundance: the absence of glove use and hand sanitation was linked to increased levels of Escherichia-Shigella, Kocuria, Enterococcus, and Corynebacterium, while the use of bleach-chlorhexidine sanitizer was associated with higher Deinococcus. These findings highlight the role of rigorous hygiene protocols in shaping the microbiota of raw goat milk and emphasize the need for targeted practices to minimize contamination risks.

The food industry faces several challenges, including intricate supply chains, compliance with food safety regulations, sustainability concerns, and the rising demand for high-quality products. Furthermore, consumers increasingly seek personalized food products with specific fat, sugar, and micronutrient levels. The ice cream industry is no exception in facing these challenges. Fortunately, Industry 4.0 technologies, such as smart manufacturing, data analytics, and the Industrial Internet of Things (IIoT), offer viable solutions to many of the aforementioned challenges. However, a deeper understanding of industrial ice cream manufacturing processes and systems is essential to apply these technologies effectively. While the related literature has often focused on ingredient selection to achieve the desired ice cream flavor and texture, there is a noticeable absence of comprehensive efforts to evaluate the impact of process- and systems-related aspects in ice cream manufacturing. This study employs a semi-systematic literature review approach to compile recent research that examines the influence of process- and system-level factors on ice cream product quality and production processes, focusing on the aspects that can benefit from implementing Industry 4.0 technologies. The literature review reveals that 1) at the process level, researchers have focused on three key processes (i.e., pasteurization, homogenization, and dynamic freezing) and their impact on the quality of the ice cream; 2) at the system level, researchers have concentrated their efforts on techno-economic factors, process scheduling, productivity, and sustainability.

Microbial and chemical properties of cheese is crucial in the dairy industry to understand their effects on cheese quality. Microorganisms within this fat, protein, and water matrix are largely responsible for physiochemical characteristics and associated quality. Prebiotics can be used as an energy source for lactic acid bacteria in cheese by altering the microbial community and provide the potential for value-added foods, with a more stable probiotic population. This research focuses on the addition of fructooligosaccharides (FOS) or inulin to the Cheddar cheese-making process to evaluate the effects on microbial and physicochemical composition changes. Laboratory-scale Cheddar cheese produced in 2 replicates was supplemented with 0 (control), 0.5, 1.0, and 2.0% (wt/wt) of FOS or inulin using 18 L of commercially pasteurized milk. A total of 210 samples (15 samples per replicate of each treatment) were collected from cheese-making procedure and aging period. Analysis for each sample were performed for quantitative analysis of chemical and microbial composition. The prevalence of lactic acid bacteria (log cfu/g) in Cheddar cheese supplemented with FOS (6.34 ± 0.11 and 8.99 ± 0.46; ± standard deviation) or inulin (6.02 ± 0.79 and 9.08 ± 1.00) was significantly higher than the control (5.84 ± 0.27 and 8.48 ± 0.06) in whey and curd, respectively. Fructooligosaccharides supplemented cheeses showed similar chemical properties to the control cheese, whereas inulin-supplemented cheeses exhibited a significantly higher moisture content than FOS and the control groups. Streptococcus and Lactococcus were predominant in all cheeses and 2% inulin and 2% FOS-supplemented cheeses possessed significant amounts of nonstarter lactic acid bacteria found to be an unidentified group of Lactobacillaceae, which emerged after 90 d of aging. In conclusion, this study demonstrates that prebiotic supplementation of Cheddar cheese results in differing microbial and chemical characteristics.

The high-throughput DNA sequencing (HTS) method is used to identify microbes in cheese and their potential functional properties. The technique can be applied to the microbiota of the cheese processing environment, raw milk, curd, whey, and starter cultures, and be used to improve the quality, safety, and other physicochemical properties of the final product. The HTS method is also utilized to study the microbiota shift of different types of cheeses during processing, as the composition and functional properties of the microbiome provide unique characteristics to different cheeses. Although there are several reviews that focused on microbiota of various types of cheeses, this review focuses on evaluating the microbiota shift of different types of cheese production and highlights key bacteria in each step of the processing as well as microbiota of various types of cheeses. KEY POINTS: • High-throughput sequencing can be applied to identify microbiota in cheese. • Microbiota in cheese is changed during making process and aging. • Starter culture plays an important role to establish microbiota in cheese.

Cheese is a fermented dairy product that is made from animal milk and is considered to be a healthy food due to its available nutrients and potential probiotic characteristics. Since the microbes in the cheese matrix directly contribute to the quality and physicochemical properties of cheese, it is important to understand the microbial properties of cheese. In this study, Cheddar cheeses produced on three different dates at the Arbuthnot Dairy Center at Oregon State University were collected to determine the microbial community structure. A total of 773,821 sequencing reads and 271 amplicon sequence variants (ASVs) were acquired from 108 samples. Streptococcus and Lactococcus were observed as the most abundant ASVs in the cheese, which were used as the starter lactic acid bacteria (SLAB). Escherichia coli was detected in the raw milk; however, it was not detected after inoculating with SLAB. According to an alpha diversity analysis, SLAB inoculation decreased the microbial richness by inhibiting the growth of other bacteria present in the milk. A beta diversity analysis showed that microbial communities before the addition of SLAB clustered together, as did the samples from cheese making and aging. Non-starter lactic acid bacteria (NSLAB) were detected 15 weeks into aging for the June 6th and June 26th produced cheeses, and 17 weeks into aging for the cheese produced on April 26th. These NSLAB were identified as an unidentified group of Lactobacillaceae. This study characterizes the changes in the Cheddar cheese microbiome over the course of production from raw milk to a 6-month-aged final product. KEY POINTS: • 271 ASVs were acquired from cheese production from raw milk to 6-month aging. • Addition of SLAB changed the microbial diversity during Cheddar cheese making procedure. • NSLAB were detected more than 15 weeks after aging. Graphical Abstract.

Understanding the microbial community of cheese is important in the dairy industry, as the microbiota contributes to the safety, quality, and physicochemical and sensory properties of cheese. In this study, the microbial compositions of different cheeses (Cheddar, provolone, and Swiss cheese) and cheese locations (core, rind, and mixed) collected from the Arbuthnot Dairy Center at Oregon State University were analyzed using 16S rRNA gene amplicon sequencing with the Illumina MiSeq platform (Illumina, San Diego, CA). A total of 225 operational taxonomic units were identified from the 4,675,187 sequencing reads generated. Streptococcus was observed to be the most abundant organism in provolone (72 to 85%) and Swiss (60 to 67%), whereas Lactococcus spp. were found to dominate Cheddar cheese (27 to 76%). Species richness varied significantly by cheese. According to alpha diversity analysis, porter-soaked Cheddar cheese exhibited the highest microbial richness, whereas smoked provolone cheese showed the lowest. Rind regions of each cheese changed color through smoking and soaking for the beverage process. In addition, the microbial diversity of the rind region was higher than the core region because smoking and soaking processes directly contacted the rind region of each cheese. The microbial communities of the samples clustered by cheese, indicated that, within a given type of cheese, microbial compositions were very similar. Moreover, 34 operational taxonomic units were identified as biomarkers for different types of cheese through the linear discriminant analysis effect size method. Last, both carbohydrate and AA metabolites comprised more than 40% of the total functional annotated genes from 9 varieties of cheese samples. This study provides insight into the microbial composition of different types of cheese, as well as various locations within a cheese, which is applicable to its safety and sensory quality.

Whey production can be an economic and environmental problem for small creameries and acid whey producers. The fermentation and distillation of whey not only eliminates the cost of disposing whey as waste while minimizing environmental impact but adds a revenue option through production of a value-added product. Kluyveromyces marxianus is typically utilized to ferment the pasteurized and pretreated whey. The fermented product contains approximately 3% ethanol v/v. Various options for distilling may be utilized such as a simple two-pot system or a more complex four-stage system to assure production of a neutral spirit. Quality of the distilled spirit is impacted by whey source, whey pretreatment, fermentation conditions, and the distilling process.

The transfer efficiency of lipid supplements rich in PUFAs such as flaxseed into blood is often poor because PUFAs are hydrogenated in the rumen. To prevent ruminal biohydrogenation of PUFA, various methods of PUFA protection have been tested with limited success. In this study, a novel method to “rumen-protect” flaxseed is proposed, which encapsulates flaxseed using a proprietary method (12BT40; N3Feed® LLC; Tualatin, OR). To determine whether 12BT40 increases omega-3 concentrations in bovine serum more than its ingredients alone, we used a double 3 × 3 Latin square design; 6 mid- to late-lactation, pregnant Holstein cows (1 block each for primiparous and multiparous cows) were fed 0 kg/d (Negative Control), 3 kg/d of 12BT40 (Treatment), and 3 kg/d of the unprocessed ingredients of 12BT40 (Treatment Control) as top-dressing for 2-week periods each. Serum samples were collected at the end of each 2-week treatment period and analyzed for their fatty acid profile, respectively. Data were analyzed using PROC MIXED in SAS version 9.4. Fixed effects were treatment, period, and parity. Repeated measures within cows were modeled with the random statement. Compared with Treatment Control, 12BT40 increased total serum fatty acid concentrations from 148 ± 18 to 183 ± 18 μg/mL (P = 0.03) (Negative Control, 138 ± 18 μg/mL). 12BT40 supplementation increased serum omega-3 concentrations from 25.4 ± 2.9 μg/mL to 34.6 ± 2.9 μg/mL (P = 0.01) (Negative Control, 16.4 ± 2.9 μg/mL). Thus, we conclude that 12BT40 is effective in increasing total fatty acid concentrations and omega-3 concentrations in bovine serum beyond what can be achieved with feeding an equal amount of ground flaxseed.

In humans, circulating omega-3 fatty acids are associated positively with health outcomes and lower chronic inflammation. To determine whether a flaxseed containing lipid supplement 12BT40 (N3Feed® LLC; Tualatin, OR) increases omega-3 content in bovine serum, we fed 6 mid- to late-lactation, pregnant Holstein cows (1 block each for primaparous and multiparous cows) for 6 wk consecutively 0 (Control; 1 week), 0.91 (1 week), 1.81 (2 weeks), and 2.72 kg/d (2 weeks) 12BT40 as top-dressing. Serum samples were collected at the end of each treatment period and analyzed for fatty acid profile. Data were analyzed using PROC MIXED in SAS version 9.4. Fixed effects were supplementation rate (linear, quadratic and cubic) and parity; cow was the random effect. 12BT40 supplementation rates increased linearly (linearP =0.01) total FFA concentrations in serum by 12%. 21%, and 42% for 0, 0.91, 1.81, and 2.72 kg/d of 12BT40. The serum omega-3 concentrations increased from 8.6 to 18.2, 22.3, 28.3±3.2 µg/mL, when 0, 0.91, 1.81, and 2.72 kg/d of 12BT40 was fed (linearP <0.0001; quadraticP = 0.003); the serum omega-6 concentrations increased from 35.2 to 38.3, 41.5, and 44.2±4.9 µg/mL (linearP =0.10). As a result, the omega-6-to-omega-3 ratio in serum improved from 4.06 to 2.10, 1.87, and 1.60±0.07 (linear, quadratic and cubic allP <0.0001). Based on these results, we conclude that feeding up to 2.72 kg/d 12BT40 improves omega-3 concentrations and omega-6-to-omega-3fatty acid ratios in bovine serum.

Milk hauling is an overlooked portion of the dairy industry and its impact on raw milk quality is not well characterized. Practices are mandated by the Pasteurized Milk Ordinance; however, gaps exist in the specifics of practices that could impact downstream milk and milk product quality. Description and classification of hauling and receiving practices and their relative impact could allow for the prioritization of improved practices that could improve quality throughout the dairy industry. The objective of this study was to identify current practices that could negatively impact the microbiological quality of raw milk during hauling and receiving. This objective was approached from two angles: (1) An industry survey was conducted to characterize milk hauling and receiving practices, and (2) a database that represented 2-yr of differences in raw (IBC) and PI (PIC) counts between receivers and producers (n = 23,285 tanker loads) was analyzed to identify and quantify hauling situations that have a negative impact on milk quality. Dairy processing facilities (n = 14) were asked to participate in the survey, of which 10 responded (78% response rate). The majority of facilities (10/14) utilized repeated tanker use per 24 h; however, facilities that only receive a few tankers a day washed after each load (n = 4). Frequency of CIP system validation greatly varied among facilities (daily to annually). This suggests that facilities that do not frequently validate could potentially have underlying CIP issues that could have a negative impact on tanker cleaning efficacy. For the database analysis, negative impact was defined as the top 2.5% of instances (n = 583) where the tanker and producer load average difference was ³ 7.67 IBC/mL and ³ 61.5 PIC/mL. Negative impact was more pronounced in PI counts. There was not an identifiable trend in seasonality. The analysis demonstrated that in instances of negative impact, the load typically included milk from a producer with historically high counts. This study suggests that CIP validation frequency and route management may need increased attention to minimize the impact hauling and receiving practices have on raw and downstream product quality.

The concept of terroir is rapidly gaining importance in the U.S. marketplace in alignment with the growth of the local food sector. Terroir for products such as coffee and wine are well recognized, but there is little science to support terroir for dairy products. Nevertheless, it's common to see promotions for regional cheeses such as Wisconsin cheese or New York Cheddar. This presentation will evaluate factors that influence farm and regional milk sources and their impact on cheese characteristics. Our current research has focused on the simple question: If all other factors are kept constant, will milk from different farms and regions produce cheeses that are different? Initial results have demonstrated that milk from farms, selected due to similar herd management principles, produce Cheddar cheeses that are different based on sensory and flavor chemistry profiles. Non-starter lactic acid bacteria (NSLAB) profiles are unique to individual farms. The link to the individual farms (terroir effect) is more pronounced in 5 mo aged Cheddar than in 9 mo aged Cheddar. Milk from coastal regions appears to be particularly suited for cheese production, likely due to complex NSLAB profiles and flavor development.

Non-starter lactic acid bacteria (NSLAB), which include lactobacilli, are found at low levels in fresh raw milk. Lactobacilli are important to the dairy industry because of their potential impact on the flavor and texture of yogurt, sour cream, and cheese. The objective of this study was to investigate the contribution of lactobacilli from raw milk on the microbiological profile of cheddar cheese during aging (0–6 mos). Using a standardized recipe, cheddar cheeses were made with raw milk sourced from dairies on the Oregon Coast (n = 4) and in the Willamette Valley (n = 2) and aged up to 6.5 mos at 53°C. Lactic acid bacteria counts (LAB) were determined in raw milk and cheese samples using standard serial dilution and spread plating techniques on Lactobacilli MRS Agar with anaerobic incubation at 30°C for 48 h. Isolates (n = 5–10/sample) were selected for preliminary speciation using high resolution melt analysis (HRM) PCR assay targeting the V1 region of the 16S rDNA. Strains were further subtyped using a second HRM repetitive sequence-based PCR (rep-PCR). Lactobacillus curvatus and L. paracasei were identified in raw milk sourced from the Oregon Coast. Lactobacillus paracasei was also identified in the Willamette Valley cheeses. Species diversity decreased throughout aging in all cheeses with the exception of cheese made from milk sourced from a single dairy on the southern Oregon Coast. After 6.5 mos of aging, the predominant species across all cheeses was L. paracasei (40.8% of the identified isolates). Strain diversity was highest in milk sourced from the northern Oregon Coast, with six unique strains of L. paracasei. Evidence suggests that milk sourcing impacts the strain diversity of NSLAB present in raw milk and cheddar cheese.

Artisan cheese makers lack access to valid economic data to help them evaluate business opportunities and make important business decisions such as determining cheese pricing structure. An economic model was developed in Excel following close collaboration with current and future artisan cheese companies. The objective of this study was to utilize this economic model to evaluate the net present value (NPV), internal rate of return, and pay back period for artisan cheese production at different annual production volumes for a given cheese type. The model is also used to determine the minimum retail price necessary to assure positive NPV for 5 different cheese types produced at 4 different production volumes. These 2 scenarios demonstrate important business considerations facing artisan cheese makers. For example, a small size cheese maker with annual production volume at 3,401 kg (7,500 lb) cannot be economically viable (negative NPV) if selling cow milk Gouda for $48.50/kg ($22/lb); by doubling the production size, the business would obtain a positive NPV. Due to differences in cheese yield, investment in aging facility, labor required during aging, and raw milk purchase price, fresh cow milk cheeses such as fresh mozzarella can be sold for about half the price of hard, aged, goats’ milk cheeses at the largest volume or about 2-thirds the price at the lowest volume examined. For example, for the given model assumptions, at an annual production of 13,608 kg cheese (30,000 lb), a fresh cows’ milk mozzarella should be sold at a minimum retail price of $27.29/kg ($12.38/lb) while a goats’ milk gouda needs minimum retail price of $49.54/kg ($22.47/lb) for the business to have NPV at or above zero. The model is utilized within the OSU Extension program and has gone through 2 major updates. The observations derived from the model are consistent with the current business situation for artisan cheese companies.

A less examined facet of the local foods movement is the impact of the location of the producer on the feasibility of operating as a local supplier. Obvious variables are labor hours in travel to regional markets and fuel expenditures. The costs these engender is strongly related to the density of customers that are willing or able to deal with smaller scale delivery. Whether supplying produce or a value-added food, the viability of an enterprise which hopes to diversify its markets or products through a local channel is dependent on that density. Furthermore the price received varies greatly depending on whether the customer is a farmers market consumer, a local grocer or restaurant, or-as those markets are exhausted-a distributor. With value-added products (28% of farms engaged in entrepreneurial activities are producing value –added products (Martinez 2010)) the availability of alternative channels and pricing received in them is particularly important due to the capital investment required for equipment.