1. Introduction
Dairy products possess intrinsic qualities that enhance gastrointestinal tract health and contribute to the well-being of the human microbiome. They play a pivotal role in the food industry due to their rich content of protein, calcium, and micronutrients, all of which are crucial for maintaining bone and muscle health. Among dairy products, cheese stands out as one of the most widely consumed and versatile options globally. With its diverse array of flavors and forms, cheese holds a prominent position in culinary culture, contributing significantly to dietary diversity and enjoyment.
Cheese is a fundamental ingredient in numerous culinary recipes and is often enjoyed on its own. Consequently, evaluating its quality becomes essential for consumers and the industry [
1].
In the cheese-manufacturing process, curd represents a crucial intermediate stage achieved by heating milk and introducing rennet. Rennet induces the coagulation of casein granules in the milk, resulting in the formation of curd, which settles at the bottom, accompanied by the generation of whey. However, in many cheese varieties, the natural separation of whey and curd does not occur spontaneously, necessitating the mechanical cutting of the coagulated mass into small cubes, referred to as curd grains [
2].
As demonstrated by Johnson et al. [
3], the coagulation process induced by rennet during cheese production, and consequently, the timing of curd cutting, significantly impacts cheese quality. Furthermore, Grundelius et al. [
4] investigated the influence of parameters such as pH, rennet concentration, and curd granule size, highlighting that granule size significantly impacts curd shrinkage, particularly in the early stages of the process: smaller curd granules result in more intense whey separation, a finding confirmed by Thomann et al. [
5]. The duration of cutting is known to be inversely related to the size of the granules. Therefore, to automate the process, it is essential to identify the phase where the granules start to become diffuse throughout the entire boiler [
4].
Determining the cutting time is contingent upon the rheological and microstructural properties of the curd gels, which are influenced by various factors, including milk pretreatment, composition, and coagulation conditions. Consequently, the identification of the cutting time, manually performed by a diary operator, varies across different cheese varieties and profoundly affects parameters such as moisture content, yield, and the overall quality of the cheese, as well as losses in whey fat [
2].
This challenge is notably accentuated in large-scale automated production facilities, where the variability in coagulation conditions, process alterations, and the potential for human errors introduce complexities in maintaining precise control over cutting times [
2,
6,
7].
For these reasons, integrating advanced methodologies, such as computer vision (CV) techniques, become indispensable to mitigate the issues, improve the cheese-making process, enhance production efficiency, and optimize product quality.
In light of the challenging task posed by identifying the optimal cutting time in cheese production, we approach it through the lens of anomaly detection (AD). Given the abundance of images depicting the normal condition of the milk before its cutting time, we adopted an AD setup to discern anomalies within this dataset. Since an extensive presence of curd spots indicates a possible optimal cutting time [
2], we considered the curd spot an anomaly, seeking to identify it amidst the distribution of normal curd images. By leveraging this approach, we aim to effectively identify deviations from the standard milk appearance, thereby facilitating the accurate determination of the curd and related cutting time.
Specifically, we propose adapting a deep learning (DL) technique belonging to the realm of one-class classification, termed the Fully Convolutional Data Description Network (FCDDN). This method employs a neural network to reconfigure the data such that normal instances are centered on a predefined focal point, while anomalous instances are situated elsewhere. Additionally, a sampling technique transforms the data into images representing a heatmap of subsampled anomalies. Pixels in this heatmap distant from the center correspond to anomalous regions within the input image. The FCDDN exclusively utilizes convolutional and pooling layers, thereby constraining the receptive field of each output pixel [
8]. Moreover, we also compared our findings with more classical machine learning (ML) approaches, specifically trained with handcrafted (HC) and deep features. The latter were extracted by pre-trained convolutional neural network (CNN) architectures.
The contributions of this paper can be summarized as follows:
Investigate the optimal cutting time: We conducted a feasibility study by introducing a novel AD-based approach to determine the optimal cutting time during curd formation in cheese production.
Development of a one-class Fully Convolutional Data Description Network: We propose and implemented a one-class FCDDN to identify curd formation by treating it as an anomaly to verify against the milk in its usual state.
Comparison with shallow AD methods: We compared the proposed approach with shallow learning methods to emphasize its robustness in this scenario on different sets of images.
High accuracy in AD: The proposed approach achieved encouraging results with F1 scores of up to 0.92, demonstrating the effectiveness of the method.
Application in the dairy industry: This work investigates if the curd-firming time identification can be achieved with an AD-based approach and, at the same, aims to provide a non-invasive, non-destructive, and technologically advanced solution.
The rest of the manuscript is organized as follows.
Section 2 provides a comprehensive review of existing methodologies for analyzing milk coagulation and AD techniques.
Section 3 elucidates the details regarding the dataset, feature-extraction methodologies, classifiers adopted, and evaluation measures.
Section 4 delves into the experimental evaluation conducted, offering a presentation of the undertaken experiments along with the corresponding results and subsequent discussions. The concluding remarks of this study, along with insightful suggestions for potential enhancements and avenues for future research based on our findings, are given in
Section 5.
4. Experimental Results
In this section, we comprehensively explore the interpretation and implications of the results derived from our study. We structure our analysis into three distinct sections: Firstly, we present the outcomes obtained using various shallow learning classifiers with handcrafted features and deep learning-based features. Following this, we discuss the results achieved by employing an FCDDN as an AD method. However, to simplify our discussion, we report only the best-performing pairs of classifiers and features. This systematic approach provides a detailed examination of the efficacy and nuances of each method employed, highlighting valuable insights into their respective performance. Finally, we provide a global experiment result analysis.
4.4. Discussion
Based on the results, the FCDDN achieved an average F1 score of 0.91 and 0.89 for the non-target and target classes, respectively. This demonstrates its ability to accurately identify standard coagulation patterns and deviations, outperforming other evaluated methods. It also showed more stable prediction across different sets.
With ML classifiers, the deep features generally outperformed the HC features, and XceptionNet provided the best performance. Furthermore, the OCSVM outperformed the IF for all sets except three in the deep features setting. Despite the key advantage of the IF and OCSVM in their efficiency in handling high-dimensional data, the results have clearly shown that the IF struggled with datasets containing structured anomalies, which may be the case. Similarly, the OCSVM’s performance on the non-target class may be sensitive to the choice of the kernel function and parameters, such as the nu parameter, which controls the trade-off between the margin size and the number of outliers.
The evaluated FCDDN provides a promising solution for automatically determining optimal cutting time. However, it is important to note that this work is a feasibility study relating to the problem under consideration. We must consider how similar datasets do not exist for the state of the art and that, to verify the generalizability of the proposed solution, additional datasets must be refined, even with synthetic data, as already in use in other contexts, from from video surveillance [
85,
86] to healthcare [
87] and face detection [
88].
Synthetic data are being used in CV tasks for object and AD. This provides a controlled environment for generating diverse data when real-world data are scarce [
89]. In the context of cheese production, simulating the cheese-formation process and creating synthetic images could improve the models’ generalization capabilities. However, synthetic data should closely resemble real-world conditions to ensure effective knowledge transfer to real-world scenarios. This aspect is particularly challenging in this scenario due to the physical and chemical changes that occur during cheese formation.
An additional consideration pertains to the potential necessity of re-training the system after its deployment in the dairy industry. Specifically, under the acquisition conditions outlined in this study, re-training the system is not required as the proposed pipeline is robust and not susceptible to domain shift, given that the acquisition condition is consolidated within the system pipeline. However, re-training may become essential to preserve accuracy and effectiveness under varying acquisition conditions. For instance, if the system is deployed in a dairy industry with different operational conditions, the production variables may change, potentially impacting the performance of the anomaly-detection model. Consequently, re-training the model with new data in such contexts will enable it to adapt to these changes, ensuring the detection of the optimal cutting time and maintaining the cheese quality.
In any case, with the refinement of additional datasets, real-world industrial implementation has the potential to enhance production efficiency and quality control.
Author Contributions
Conceptualization, A.L. and C.D.R.; methodology, A.L.; software, A.L. and D.G.; validation, A.L., D.G., A.P., L.Z., B.P. and C.D.R.; formal analysis, A.L., D.G., A.P., L.Z., B.P. and C.D.R.; investigation, A.L.; resources, A.L. and D.G.; data curation, A.L.; writing—original draft preparation, A.L., A.P., L.Z., B.P. and C.D.R.; writing—review and editing, A.L., A.P., L.Z., B.P. and C.D.R.; visualization, A.L., D.G., A.P., L.Z., B.P. and C.D.R.; supervision, B.P. and C.D.R.; project administration, C.D.R.; funding acquisition, C.D.R. All authors have read and agreed to the published version of the manuscript.
Funding
We acknowledge financial support under the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.5—Call for tender No. 3277 published on 30 December 2021 by the Italian Ministry of University and Research (MUR) funded by the European Union—NextGenerationEU. Project Code ECS0000038—Project Title eINS Ecosystem of Innovation for Next Generation Sardinia—CUP F53C22000430001-Grant Assignment Decree No. 1056 adopted on 23 June 2022 by the Italian Ministry of University and Research (MUR).
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Acknowledgments
We thank Massimiliano Sicilia of LAORE Sardegna and “Podda Formaggi” dairy industry for the dataset acquisition and Gianluca Dettori of “BiosAbbey” for supporting this study.
Conflicts of Interest
The authors declare no conflicts of interest.
Abbreviations
The following abbreviations are used in this manuscript:
CV | computer vision |
AD | anomaly detection |
DL | deep learning |
FCDDN | Fully Convolutional Data Description Network |
HC | handcrafted |
CNN | convolutional neural network |
CH | Chebyshev moment |
LM | Legendre moment |
ZM | Zernike moment |
Haar | Haar feature |
HARri | rotation-invariant Haralick features |
LBP | Local Binary Pattern |
Hist | grayscale histogram feature |
OCSVM | one-class SVM |
IF | Isolation Forest |
VAE | Variational Autoencoder |
GAN | Generative Adversarial Network |
RNN | Recurrent Neural Network |
LSTM | Long Short-Term Memory |
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