# Breakthrough innovations in industrial cheesemaking processes

Thomas Croguennec, Yves Le Loir, Romain Jeantet

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Thomas Croguennec, Yves Le Loir, Romain Jeantet. Breakthrough innovations in industrial cheesemaking processes. Current Opinion in Food Science, 2025, 62, pp.101267. 10.1016/j.cofs.2024.101267. hal-04873404

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Review

# Breakthrough innovations in industrial cheesemaking processes

Thomas Croguennec, Yves Le Loir and Romain Jeantet

<figure><img src="image_2.png" /></figure>

Cheese, which has played a crucial role in mankind history, is now produced using optimized and automated processes. However, these productions still have significant potential for improvement in terms of the technological, economic, and environmental performance and of the product quality. Controlling milk composition and curd cutting time pave the route for optimizing cheesemaking performances and cheese characteristics, while ripening time can be reduced by uncoupling cheese texture and aroma construction or properly selecting cheese ripening strains and enzymes. Omics techniques now make it possible to screen starters both having technological and functional or probiotic properties or preventing fungal or bacteria spoilage. Similarly, the mining of the whole processing data set using artificial intelligence, expert knowledge, and multi-objective optimization has enormous potential for identifying the best possible cheesemaking route. By taking environmental performance into account, the latter can identify solutions that reduce consumptions of water, energy, and cleaning agents.

**Address**

INRAe, Institut Agro, STLO, 35042 Rennes, France

Corresponding author: Jeantet, Romain (romain.jeantet@institut-agro.fr)

**Current Opinion in Food Science 2025, 62:101267**

This review comes from a themed issue on **Food Engineering & Processing**

Edited by Xiao Dong Chen

Available online 24 December 2024

https://doi.org/10.1016/j.cofs.2024.101267

2214-7993/© 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

**Introduction**

Cheesemaking is an ancestral method used to extend the shelf-life of major milk nutrients (proteins, namely, casein — caseus is the Latin root of cheese — and fat) while obtaining a product with desirable organoleptic properties

(texture and flavors) that is easy to handle and store (shape). More than 1000 traditional cheeses are listed worldwide, and they all share common processing steps. In brief, cheesemaking begins by coagulating the milk using proteolytic enzymes, lactic acid bacteria (LAB), or a combination of both, followed by the molding of the milk gel to ensure or to complete whey drainage, which can be started in a vat, and the salting of the fresh cheese. These steps provide the required cheese shape, moisture and salt contents, and pH that define the biological and biochemical dynamics during ripening and therefore the shelf-life of the ripened cheese. For most of cheese history, the proper conduct of these steps has been controlled by the cheesemaker's sensory perceptions [1].

Nowadays, cheeses are mostly produced in large, automated dairy plants to satisfy the massive consumer demand (> 20 million tons per year worldwide). Therefore, the cheesemaker's expertise was progressively replaced by precise flow diagrams comprising a succession of automated or operator-triggered technological steps with predefined operating parameters and performance indicators to guarantee production efficiency, safety, cheese quality, and reproducibility. The main technological steps encountered in modern industrial cheesemaking process include (i) heat treatments, high-speed centrifugation (bactofugation), and microfiltration to reduce endogenous microbial load and eradicate pathogens of raw milk, (ii) centrifugation (skimming) and ultrafiltration to fix the protein/fat ratio of cheese milk and if necessary to reduce its lactose content to control the acidification of the curd, (iii) the addition of selected coagulants, starters, and cheese ripening microorganisms in order to control biological and biochemical changes during cheesemaking and aging, and (iv) programmed sequences for cutting the milk gel, stirring and, if necessary, heating the whey-and-curd grains mixture prior molding and eventually pressing the curd in order to control the moisture content, pH, and the colloidal calcium content of the cheese at demolding.

Despite the precautions taken to precisely follow cheese flow diagram, disparities in performance indicators (e.g. cheese yield, fat, and protein recoveries in cheese, taste out of organoleptic standards) persist depending on the cheese plant or between batches within the same plant. Recent advances, supported by improved scientific knowledge, in processing technology (section *Advances in cheese processing technology*) and biotechnology and microbiology (section *Advances in cheese biotechnology and cheese*
*microbiology*) are helping to reduce performance gaps and design innovative cheesemaking routes. In large cheese manufacturing plants, hundreds of operating parameters and process indicators are followed, recorded, and stored but are not analyzed through a holistic approach. Their use in real time to improve cheese production planning and as a tool for predicting cheesemaking performances or for multi-objective optimization is now possible thanks to artificial intelligence technologies (section *Data analytics for cheese production optimization*). Cheesemaking business is one large producer of by-product stream such as whey (80–90% of milk volume is removed as whey) and wastewater coming from cleaning sequences, generating huge volumes of effluents [2]. At the same time, it requires large amounts of water and of energy to maintain the temperature of ripening rooms and to concentrate and to spray dry whey [3]. Sustainability and environmental management are hot topics for cheesemaking plant, and this will be discussed in section *Sustainability and environmental management*.

## Advances in cheese processing technology

Cheese yield, protein, and fat recoveries are critical performance indicators in the cheese industry. Several steps are responsible for the variability in these indicators, such as the heating of cheese milk, which is used to denature whey proteins in order to increase simultaneously their retention in the curd and the cheese moisture content. At temperatures > 75°C, whey proteins denature and form aggregates with caseins [4], which amount increases with the intensity of heat treatment [5]. The presence of aggregates and the shift of mineral balance in heated cheese milk impair its enzymatic coagulation by delaying milk clotting time, reducing the kinetics of gel firming, and decreasing the final gel strength [6]. Adapting the cheesemaking parameters and correcting the mineral balance, by adding ionic calcium or acidifying the milk, are classical means to limit the drawbacks of heat treatment on cheese milk coagulation and to improve cheesemaking indicators [7]. However, the amount, the structure, and the properties of the heat-induced aggregates vary with the fine cheese milk composition, such as the whey protein/casein ratio, pH [8], mineral content, and other soluble components [9], as well as the flow regime in the heat exchanger [10]. These parameters are generally not precisely determined on an industrial scale although they are essential for defining the heat treatment conditions with a view to optimizing cheesemaking performances.

Defining the cutting time of the enzymatic gel and the intensity of the curd/whey mixture stirring to release whey from curd grains is crucial to achieve the desired cheese moisture content while minimizing fat and protein losses in whey. Cutting must take place when the gel reaches specific functional properties, such as its

ability to shrink and expel whey rapidly, to withstand cutting and stirring without shattering. At industrial scale, cutting time has long been determined on the basis of the coagulation time ($t_c$: sol-gel transition) to which a multiplication factor is applied depending on the targeted cheese moisture content. However, gels may exhibit different firmness at cutting time and potentially different structural and functional properties due to small differences in the fine composition of the individual milks [11]. With aim to increase cheese performance indicators reproducibility, modern cheese manufacturing plant use inline sensors [12] or tools near the milk coagulation vat in order to initiate cutting at a specific gel firmness [13]. Gel firmness is indicative of the structural and functional properties of the casein network. The size of the gel pores at cutting and the kinetics of decrease of the curd grains permeability immediately after cutting are considered important factors in explaining fat losses in whey [14]. The fat losses by leakage are expected to depend on the total curd grain surface and therefore are affected by the distribution of the curd grain size, which widens in case of uneven cutting or curd grain shattering. Online measurements of the curd grain size distribution using an image capture system [15] would be a nice perspective to optimize the intensities of cutting and the stirring steps and to predict fat losses in whey.

Although the majority of global cheese production still follows traditional manufacturing principles, the ‘MMV’ — Maubois, Mocquot, Vassal — patented process [16] offers an innovative approach in which milk proteins and fat are concentrated through ultrafiltration until the desired cheese composition is reached, and then this liquid precheese is coagulated. To overcome the ripening challenges of such cheeses, a process combining the mixture of this controlled liquid precheese with an aromatic base obtained through the fermentation of a dairy substrate has been recently proposed (Figure 1). By coupling draining substitution via membrane separation and ripening time reduction via an external fermentation stage, this efficient process opens new avenues for making cheeses with a large range of flavors and textures while significantly cutting water and energy consumption [17].

## Advances in cheese biotechnology and cheese microbiology

### Biotechnological production of coagulants

Enzymatic coagulation of milk begins by the hydrolysis of κ-casein by an aspartic protease and the destabilization of the casein micelles, which aggregate and form a protein network spanning the entire volume of milk. Traditionally, milk clotting enzymes (MCE) present in the stomach of unweaned calves (rennet that contains > 90% chymosin) were used, but an ongoing search of
<figure><img src="image_3.png" /><figcaption>Figure 1</figcaption></figure>

Traditional cheesemaking (a) vs innovative patented process (b) uncoupling texture (liquid precheese obtained by ultrafiltration) and aroma (fermentation of a dairy substrate) elaboration.

substitutes from other animal, plant, microbial, and re-
combinant origins now offers a wide choice of MCE on
the market, with the last two origins being predominant
[18]. MCE differ by their specificity for cleaving κ-
casein, their nonspecific proteolytic activity, their sensi-
tivity to temperature, pH, ions, and their affinity with
the curd matrix when the whey is drained off [19]. The
coagulant type affects the cheese yield by influencing
protein and fat recovery, and, in cooperation with mi-
croorganisms, they affect the kinetics of cheese ripening,
influencing the texture, the flavor, and the production of
functional peptides [20–22]. The choice of the MCE
preparation depends on the kind of cheese to be man-
ufactured: for instance, nonspecific proteolytic activity of
MCE preparations should be lower to reduce bitter taste
and extend shelf-life of soft cheeses and higher to ac-
celerate curd softening and reduce time-to-market of
semihard cheeses. At the same time, the coagulant type
affects the fine composition of cheese whey, and one
study reports an impact on its subsequent processing
steps [23].

Exploration and control of cheese ecosystems

In the early 2000s, the development of new sequencing
technologies allowed for the comprehensive and

unbiased exploration of numerous cheese ecosystems.
These studies, based on the sequencing of DNA frag-
ments common to all bacteria (typically fragments of the
16S rRNA gene) or on the shotgun sequencing of total
DNA extracted from the cheese ecosystem, revealed a
much broader spectrum of species and a previously un-
suspected microbial richness in these microbiota [24].
For instance, a combination of cultural, genomic, and
metagenomic techniques on 13 different traditional
cheeses unveiled the presence and high density and
diversity of halotolerant and halophilic bacteria whose
origins are probably multiple [25].

The rise of sequencing techniques has enabled the se-
quencing of the entire genomes present in a given
ecosystem (shotgun metagenomics approach). Through
the identification and analysis of all these genes, we gain
access to the potential functions carried by these mi-
crobiomes. Finally, the use of metatranscriptomics al-
lows for highlighting the genes actually expressed by the
cheese microbiota and, therefore, potentially the func-
tions implemented at various stages of the process (e.g.
fermentation initiation, ripening). This information
about the metabolic capabilities of strains and ecosys-
tems gives access to the tremendous biodiversity in
starter species. It allows for screening LAB on targeted
criteria and for considering the development of microbial
cultures with targeted functional properties (e.g. pro-
duction of vitamins, flavors, texturizing compounds)
[26]. Although unauthorized in the European Union,
genetic engineering of starter cultures (e.g. through
CRISPR-CAS technique) is also considered as a means
to boost beneficial or organoleptic properties of microbial
species [27].

Beyond sequencing technologies, the development of
rapid and high-throughput techniques and sensitive
sensors might soon allow a fine-tuned and online mon-
itoring of the fermentation process (e.g. population dy-
namic, expression of technofunctional properties in
cheese microbiota, early detection of spoiling micro-
organisms, of pathogens, or of metabolites). With the
help of artificial intelligence tools (see section *Data
analytics for cheese production optimization*, hereafter), on-
line analysis of these massive data might offer innovative
means to monitor cheesemaking processes in real time.

The development of ‘2-in-1’ cultures can provide in-
novation opportunities (Figure 2). Thus, to eliminate
certain antifungal additives (such as sorbate or nata-
mycin) used in products like sour cream or grated Em-
mental, which are susceptible to fungal contamination,
strains of LAB have been screened for their ability to
express natural antifungal activity. These 2-in-1 cultures
play a role in both the product manufacturing and its
preservation against fungal spoilage [28–30].

This inhibitory potential can also be sought to counter
contamination by pathogens. Through acidification,
starter LAB contribute to inhibit the growth of many
undesired bacteria. Some bacterial strains also produce
bacteriocins, a type of antibacterial peptide targeting
other species, such as *Listeria monocytogenes* [31] or *Sta-
phylococcus aureus*. Nisin, produced by some *Lactococcus
lactis* strains, is probably the most well-documented
bacteriocin. Nisin-producing *L. lactis* strains can be used
in cheesemaking process to inhibit the growth of spoi-
lage microorganisms such *Clostridium tyrobutyricum* [32].

Another aspect of the ‘2-in-1’ approach is the selection of
cultures, which gather technofunctional and probiotic
properties. For example, some strains of
*Propionibacterium freudenreichii*, involved in the produc-
tion of Swiss-type cheeses exhibit anti-inflammatory
properties. Cheese can thus be a delivery vehicle for
probiotic activity. Moreover, the cheese matrix con-
tributes to the protection of these strains against diges-
tive stress and improves their survival [33]. Similarly,
dairy fat enhances the anti-inflammatory effect of these
strains [34]. It is therefore important to consider the
bacteria–dairy matrix interactions in the formulation of
functionally targeted cheeses.

Data analytics for cheese production
optimization

Optimizing the cheesemaking process is a complex
multivariable problem in which various performance
objectives are at stake: economic, product quality, pro-
duct safety, environmental to name a few. In the
meantime, a large number of data (e.g. processing
parameters, process control indicators, performance in-
dicators) is generated throughout the cheesemaking
process, from each milk batch onward. These data are
often fragmented and heterogeneous. Relationships be-
tween processing parameters and indicators may be
complex, that is, they are not always represented by
known analytical models. However, data-driven mod-
eling, based on in-depth analysis of the collected data,
expert knowledge, and artificial intelligence approaches,
enables those complex relationships to be represented,
leading to generic knowledge [35]. Such an approach
consists, for example, to correctly predict the whole
cheese ripening process from biochemical and sensory
indicators coupled with dynamic Bayesian model and
different pieces of expert knowledge [36] or proteolysis
via machine learning [37], in order to ensure optimal
cheese ripening and sensory characteristics. Similarly,
multi-objective optimization approach integrating ana-
lytical models and expert knowledge was successfully
used to identify low-cost innovative pathways to conduct
milk microfiltration [38]. The next challenge for cheese
industry is to correctly represent the whole cheese-
making process by a combination of analytical and data-
driven models in order to monitor, optimize, predict, and
facilitate cheese production decision-making in a multi-
objective optimization approach as is already done in
other industrial sectors [39,40].

Continuous, real-time monitoring and processing of
collected data through Internet-of-Things technology
enable dynamic responses to deviations from standard
production conditions [41]. The development of in-
tegrated and/or portable instruments and sensors for in
situ measurements to control production processes in real
time by the use of connected devices and systems al-
lowing continuous data collection and exchanges has
gained increasing attention in the cheese industry. X-ray
imaging, ultrasound velocity, infrared spectroscopy,
Raman, fluorescence, and nuclear magnetic resonance
spectroscopy spectroscopies, and computer vision com-
bined with machine learning techniques are, among
others, efficient tools to provide real-time information on
cheese characteristics during processing [42–47].

Sustainability and environmental
management

Cheese production consumes a high amount of energy
and water (mainly for cleaning) and generates large
amounts of by-products (whey) and wastes (wastewater,
<figure><img src="image_4.png" /><figcaption>Figure 2</figcaption></figure>

2-in-1 starters screening pipeline. 1. Microbes of food interest in biobanks; 2. *In vitro* screening on technological capacities; 3. *In vitro* screening on probiotic properties, antimicrobial capacities, etc.; 4. Phenotype characterization, omics data, and other metadata; 5. Dedicated databases and artificial intelligence; 6. *In silico* screening.

especially from equipment cleaning and food wastes due
to improper production and storage). In cheese plants,
energy consumption mainly concerns milk pasteuriza-
tion and whey treatment (evaporation and drying), while
the actual cheesemaking process is minimal [48]. Given
their high lactose content, whey and permeates proces-
sing is compatible with an innovative drying process
using thin-film rotary evaporators, offering energy sav-
ings estimated at 20-40% [49]. Indeed, the high lactose
content of these streams limits the achievement of a
cohesive nonflowing state in the concentrate obtained
during the overconcentration of the product under high
shear before it is granulated into discrete particles [50].

Cleaning accounts for a significant proportion of the cheese industry's environmental impact. Traditional cleaning sequences involve prerinsing, alkaline/acidic washing steps separated by an intermediate rinsing, sanitizing rinsing, and final rinsing in order to remove all traces of cleaning agents and contaminants. These steps, most commonly integrated in cleaning-in-place (CIP) systems that recirculate flushing solutions in sequences, are optimized to reduce water, cleaning agents, and energy consumptions through reusing water from final rinsing cycles, controlling the cleaning solutions concentration, temperature, and contact time [51]. The search for more eco-friendly cleaning procedures is part
of cheese industry's environmental responsibility. Enzymes are being explored as a more sustainable alternative to traditional chemicals and thermal treatments in CIP. They offer several advantages, such as reduced water consumption, lower operating temperatures (leading to energy savings), shorter cleaning times, and lower costs for wastewater treatment. Enzyme-based cleaning solutions are presently focused on membrane cleaning but represent an emerging area for cheese production [52]. Consumption of freshwater and production of wastewater can also be reduced by using compressed air or vacuum systems to remove fouling materials from equipment [53]. Wastewater management using reverse osmosis to recover, on the one hand, effluents rich in nutrients such as N, P, and K for the production of (bio)fertilizers [2,53] and, on the other hand, water for reuse in CIP and other processing steps (steam production, cooling water) without compromising product safety is realistic through the use of a 'water-fit-for-purpose' approach [54]. Methods are also tested to recover cleaning agents from wastewater [55].

## Conclusion and future trends

Although cheese manufacturing is becoming increasingly automated, it still has levers for improvement in terms of product quality consistency and both economic and environmental performance. Indeed, some important steps remain in the empirical know-how. For instance, the composition of the curd grains in vat up to molding is not yet controlled, even though it contributes to the variation in composition and properties of soft and hard cheeses. Beyond the examples given above, such as the gel cutting time determination using gel firmness, the use of more inline sensors will be of great help to cheesemakers for controlling the process. In this view, sensors based on fast acquisition technologies (e.g. Fourier transform infrared spectroscopy) offer promising perspectives to determine in real time the composition and/or size distribution of curd grains in vat.

At the microbial level, implementation of new omics techniques combined with highly sensitive sensors will surely lead to a better monitoring of cheesemaking processes. Beyond these fine-tuned monitoring, the development of genetic engineering in LAB or other dairy microorganisms might also lead to the development of 'Swiss army knife' starter cultures capable of performing multiple functions at the same time: ensuring techno-functional properties while contributing to organoleptic quality, longer shelf-life, food safety, or even probiotic properties. In the near future, cheesemaking industry will also have to cope with new developments in genetic engineering leading to the emergence of so-called cow-free milk and cow-free cheese (e.g. milk components produced by recombinant microorganisms), which might revolutionize the sector.

Cheese industry also faces challenges in terms of nutrition and adaptation to climate change. Salt reduction for improving the nutritional properties of cheese is a challenging task in cheesemaking as it favors whey drainage, improves cheese taste and modifies cheese texture, controls water activity and microbial activities, and reduces product shelf-life. The effect of climate change on milk composition of dairy cows used to graze on pasture land is a major concern for cheesemaking performances as it is becoming increasingly common to observe milk with poor gelation properties. The development of routine analytical tools to determine more precisely variations of milk composition during seasonal heat waves could help anticipate corrections to be made during cheese production.

## Funding

This research did not receive any specific grant from funding agencies in the public or private sectors.

## Data Availability

No data were used for the research described in the article.

## Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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For the first time within the context of food processing, the authors implement a global model based on dynamic Bayesian networks that describes microbial activities and the dynamics of sensory property development during the ripening Camembert-type cheese.

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52. Pant KJ, Cotter P, Wilkinson MG, Sheehan JJ: Towards sustainable Cleaning-in-Place (CIP) in dairy processing: exploring enzyme-based approaches to cleaning in the cheese industry. *Compr Rev Food Sci Food Saf* 2023, **22**:3602-3619, https://doi.org/10.1111/1541-4337.13206.

The authors reviewed the potential environmental, economic, and efficiency benefits of using enzymes-based cleaning-in-place (CIP) instead of chemical CIP, as well as the current limitation of its widespread use in the cheese industry.

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