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## Meet the editors

Dr. Salam A. Ibrahim is a food science research professor in the food and nutritional sciences program at North Carolina A&T State University. Dr. Ibrahim established a research program in dairy starter cultures, food safety, and probiotics. He has successfully conducted projects that were funded by the NIFA-US-DA, DHS, other funding agencies, and the private sector. Many of his funded projects have focused on the isolation of beneficial

strains and the functional characterization of related health benefits. Dr. Ibrahim became specifically interested in the characteristics of Lactobacillus bulgaricus and the effects it has on the quality of yogurt. Currently, he is interested in isolating novel bacterial strains of L. bulgaricus and other lactic acid bacteria, as well as novel delivery systems and new food applications.

Dr. Tahl Zimmerman is an expert biochemical food scientist and Food and Nutritional Sciences lecturer at North Carolina Agricultural and Technical State University. His research interests include the functionality and preservation of probiotic bacteria in dairy products and the development of novel food safety applications. Dr. Zimmerman recently identified choline kinase as an antimicrobial target in Gram-positives as well as a new technique for monitoring the activity of this enzyme. He is currently working to apply this new knowledge in the preservation of food products, including dairy products. Dr. Zimmerman has also developed several dairy-based classroom and lab exercises designed to improve students' understanding of biochemistry and other basic sciences underlying Food Sciences and has worked hard to improve the curriculum at North Carolina A&T.

Dr. Rabin Gyawali is a research scientist (microbiologist) of food science. His research focuses on various aspects of food microbiology and safety. In particular, his research interests relate to dairy science, food fermentation, probiotics and prebiotics, natural antimicrobials, food safety, and human health. To date, he has published more than twenty peer-reviewed articles and six book chapters dealing with these topics.

Contents

**Section 1**

**Section 2**

Cheeses

**Section 3**

*by Vladimir Ermolaev*

*and Howard Moskowitz*

Milk By-Products Utilization

*by Syed Mansha Rafiq and Syed Insha Rafiq*

Cases of South Africa and Zimbabwe

*by Chari Felix and Ngcamu Bethuel Sibongiseni*

Mind Genomics, and Predictive Viewpoint Typing *by Ryan Zemel, Somsubhra Gan Choudhuri, Attila Gere, Himanshu Upreti, Yehoshua Deite, Petraq Papajorgji*

and Technology

**Preface III**

Introduction **1**

**Chapter 1 3**

Dairy Food Processing Technology **11**

**Chapter 2 13**

**Chapter 3 31**

Supply Chains and Consumers **39**

**Chapter 4 41**

**Chapter 5 59**

Introductory Chapter: Overview of Trends in Dairy Science

*by Tahl Zimmerman, Rabin Gyawali and Salam A. Ibrahim*

Study of the Kinetics of Vacuum Drying of Hard and Semihard

A Synthesis of Risks in Dairy Value Chains in Southern Africa:

Mind, Consumers, and Dairy: Applying Artificial Intelligence,

## Contents

Preface

The dairy industry has faced several challenges that impact dairy food quality and consumer acceptability. As a response to these challenges, this book presents a different approach to address these issues facing the dairy industry and includes seven chapters dealing with dairy processing, probiotic characteristics, and current issues related to consumers. The book is divided into four sections and begins with an introductory section that describes the background and history of dairy science along with recent developments. The second section is related to dairy processing technology and covers drying processes and by-product utilization. The third section of the book consists of two chapters that address dairy supply chain risks and issues related to consumer decisions about dairy products. Finally, the last section deals with dairy foods as functional food products and the probiotic

**Salam A. Ibrahim, PhD, Tahl Zimmerman, PhD, and Rabin Gyawali, PhD**

North Carolina A&T State University, Greensboro, North Carolina, USA

characterization of yogurt starter culture.

*by Ayowole Oyeniran, Rabin Gyawali, Sulaiman O. Aljaloud,*

*Albert Krastanov and Salam A. Ibrahim*


6. Legislation and policies

For any management system to be successful, efforts from the municipalities, manufacturers, and residents must be combined. Legislation that assigns responsibility for hazardous components and clarifies handling requirements of household hazardous products encourages manufacturers to consider sustainable methods of recycling waste from their products. Collection programs and proper management schemes fostered by municipalities and industries working together can reduce the amount of HHW that is discarded dangerously. At the core of these programs is the voluntary source separation by residents within their households. The participation of municipalities, manufacturers, and retailers is also required for these programs to be successful at HHW management (Figure 7) [1].


**Selected Papers from XVI MaNaPro and XI ECMNP**

Editors

**Susana P. Gaud ˆencio Rui Pedrosa Vitor Vasconcelos**

MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade • Manchester • Tokyo • Cluj • Tianjin

*Editors* Susana P. Gaudencio ˆ UCIBIO, NOVA School of Science and Technology, NOVA University of Lisbon Portugal

Rui Pedrosa MARE—Marine and Environmental Sciences Centre, Polytechnic of Leiria Portugal Vitor University Portugal

Vasconcelos CIIMAR/CIMAR, of Porto

*Editorial Office* MDPI St. Alban-Anlage 66 4052 Basel, Switzerland

This is a reprint of articles from the Special Issue published online in the open access journal *Marine Drugs* (ISSN 1660-3397) (available at: https://www.mdpi.com/journal/marinedrugs/ special issues/MaNaPro-ECMNP-2019).

For citation purposes, cite each article independently as indicated on the article page online and as indicated below:

LastName, A.A.; LastName, B.B.; LastName, C.C. Article Title. *Journal Name* **Year**, *Volume Number*, Page Range.

**ISBN 978-3-0365-0642-5 (Hbk) ISBN 978-3-0365-0643-2 (PDF)**

© 2021 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license, which allows users to download, copy and build upon published articles, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications.

The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons license CC BY-NC-ND.


**5. Discussion and conclusions**

Patients with brain metastases have usually a short survival. Historical studies have demonstrated that with no treatments the survival is of the order of one - two months, due to systemic progression in sites other than brain. Although brain metastases incidence is increasing, there is no consensus for their treatment. Currently, treatment options include WBRT, surgery, chemotherapy and SRT/CRT. WBRT has the advantage of being easily and widely available and is able to extend survival to three to six months. In the case of solitary metastases, the addition of surgical resection to WBRT has doubled the survival, in the range of 10 - 12 months (Patchell et al. 1990, Heilbrun and Adler 2010). However, many patients have metastases in locations not amenable to surgical resection. STRT, for these patients has demonstrated in terms of survival and palliation to be a reliable clinical tool

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1. Introduction

Images are often the most important category among the available digital data. In the recent years, image data is increasing and will continue increase in the near future. Since it is difficult to deal with large amount of image data as the available data increases, it becomes crucial to use the automated tools for various purposes in connection to image data. The image processing provides wide range of techniques to deal with the images. By using the image processing techniques, we can make the work much easier not only for now, but also for the future when there will be more data and more work to do on the images.

Image segmentation is an essential image processing technique that analyzes an image by partitioning it into non-overlapped regions each region referring to a set of pixels. The pixels in a region are similar with respect to some characteristic such as color, intensity, or texture [1]. The pixels significantly differ with those in the other regions with respect to the same characteristic [2–4]. Image segmentation plays an important role in a variety of applications such as robot vision, object recognition, medical imaging and etc. [5–7]. Image segmentation approaches can be divided into four categories. They are thresholding, edge detection, region

extraction and clustering. Clustering techniques can be used for data segmenting image data as they are used for partitioning large datasets into groups according to the homogeneity of data points.

In clustering, a given population of data is partitioned into groups such that objects are similar to one another within the same group and are dissimilar to the objects in other groups [8, 9]. There are different categories of clustering techniques. These can be partitional (hierarchical and non-hierarchical), like K-means, PAM, CLARA, CLARANs [10, 11]; model-based, like Expectation Maximization, SOM, Mixture model clustering [12, 13]; or fuzzy-based like Fuzzy C-Means [14, 15].

Partitional clustering techniques attempt to break a population of data into some predefined number of clusters such that the partition optimizes a given criterion.

Formally, clusters can be seen as subsets of the given dataset. So, clustering methods can be classified according to whether the subsets are fuzzy or crisp (hard). In hard clustering, an object either does or does not belong to a cluster. These methods partition the data into a specified number of mutually exclusive subsets. However, in fuzzy-based clustering, the objects may belong to several clusters with different degrees of membership [16].

It is studied in the literature that many researchers experimented with the Fuzzy C-Means (FCM) algorithm in a wide variety of ways for achieving better image segmentation results [1, 17]. In [18], a penalized FCM (PFCM) algorithm is presented for image segmentation for handling noise by adjusting a penalty coefficient. The penalty term used here takes the spatial dependence of the objects into consideration, which is modified according to the FCM criterion. In [19], a fuzzy rule-based technique is proposed. It employs the rule-based neighborhood enhancement system to impose spatial continuity by post-processing on the clustering results obtained using FCM algorithm. In [20], a Geometrically Guided FCM (GG-FCM) algorithm is proposed, which is based on a semi-supervised FCM technique for multivariate image segmentation. In [21], a regularization term was introduced into the standard FCM to impose the neighborhood effect. In [22], this regularization term is incorporated into a kernel-based fuzzy clustering algorithm. In [23], this regularization) term is incorporated into the adaptive FCM (AFCM) algorithm [24] to overcome the noise sensitivity of AFCM algorithm.

However, it is found in the literature that a very less attention is paid towards the hybridization of clustering techniques for partitioning the datasets.

The present research work aims at developing hybrid clustering algorithms involving K-Means and Fuzzy C-Means (FCM) techniques for achieving better clustering results. As part of hybridization, two algorithms are developed, KMFCM and KMandFCM. The KMFCM algorithm first performs K-Means on the dataset and then performs FCM using the results of K-Means. The KMandFCM algorithm performs K-Means and FCM in the alternative iterations.

All the experiments are carried out using the datasets that are related to four images. For performance evaluation, CPU time, clustering fitness and sum of squared error (SSE) are taken into consideration.

The following sections provide a detailed discussion of K-Means (KM), Fuzzy C-Means (FCM), KMFCM and KMandFCM algorithms.

### 2. The K-Means (KM) algorithms

Partitional clustering methods are appropriate for the efficient representation of large datasets [11]. These methods determine k clusters such that the data objects in a cluster are more similar to each other than to the objects in other clusters.

#### Segmenting Images Using Hybridization of K-Means and Fuzzy C-Means Algorithms DOI: http://dx.doi.org/10.5772/intechopen.86374

The K-Means is a partitional clustering method, which partitions a given dataset into a pre-specified number, k, of clusters [25]. It is a simple iterative method. The algorithm is initialized by randomly choosing k points from a given dataset as the initial cluster centers, i.e., cluster means. The algorithm iterates through two steps till its convergence:

The algorithm for K-Means is as follows [26]. Here, k represents the number of clusters, d represents the number of dimensions or attributes, Xi represents the ith data sample, μj (j = 1, 2, …, k) represents the mean vector of cluster Cj, t is the iteration number. For termination condition the algorithm computes percentage change, Eq. (2). The algorithm terminates when Percentage change < α. Here, α is assumed to be 3 since it is negligible.

#### KM algorithm

$$d\left(\mathbf{X}\_i, \mu\_j\right) = \sqrt{\sum\_{l=1}^d \left(\mathbf{x}\_{il} - \mu\_{jl}\right)^2} \tag{1}$$

3.Update mean vectors μj ( j = 1, …, k).

4.Compute Percentage change as follows

$$\text{Percentage change} = \frac{|\Psi\_t - \Psi\_{t+1}|}{\Psi\_t} \times 100\tag{2}$$

where Ψt is the number of vectors assigned to new clusters in tth iteration and Ψt + 1 is the number of vectors assigned to new clusters in (t + 1)th iteration.

5.Stop the process if Percentage change < α, otherwise set t = t + 1 and repeat the steps 2–4 with the updated parameter.

The K-Means uses Euclidean distance as a proximity measure for determining the closest cluster to which a data object is assigned [13]. The algorithm stops when the assignment of data points to the clusters no longer changes or some other criterion is satisfied. The K-Means is a widely used algorithm for clustering and it requires less CPU time. However, it mainly suffers from detecting the natural clusters that have non-spherical shapes or widely different sizes or densities [25].


**6.8 Anti-inflammatory activity**

The leaves of *A. aspera*are used for the treatment of dermatoligical disorder [53]. It was investigated that alcoholic extract of *A. aspera*has potential anti-inflammatory effect in Wistar rats at different doses such as 50, 100 and 200 mg/kg. Anti-inflammatory effect of *A. aspera* extract carried out by using carrageenan-induced paw edema (acute inflammatory model) and cotton pellet granuloma test (chronic inflammatory model). All the doses of alcoholic extract of *A. aspera* tested in carrageenan-induced paw edema caused a significant (*p* < 0.05) and decreased paw edema 32–40.5 compared to control group. In cotton pellet granuloma model, the concentration of *A. aspera* (50, 100 and 200 mg/kg) caused a significant (*p* < 0.05) and marked inhibition 34.6% of granuloma weight as compared to control group [54].

*Comparative Study between Herbal and Synthetic Antidepressant Drugs DOI: http://dx.doi.org/10.5772/intechopen.103977*

## **6.9 Antipyretic activity**

In a study of anti-pyretic activity of crude extract of *A. aspera* was carried out by brewer's yeast-induced pyrexia in rats. Initially rectal temperatures were recorded. Administration of the yeast at a dose 10 mg/kg to the rats produced significant increase in rectal temperature 24 hours after yeast injection [55]. The methanolic extract of leaves of *A. aspera* consist of alkaloids, steroids, proteins, flavonoids, saponins, mucilage, carbohydrates and tannins. The methanolic extract was administered orally to different groups at a dose of 100 and 200 mg/kg by intra-peritoneal route. 0.3 ml of normal saline was administered to the control group. Paracetamol (150 mg/kg) used as a standard drug. The methanolic extract of *A. aspera* demonstrated significant (*p* < 0.01) antipyretic activity. The dose at a 200 mg/kg extract has shown a good antipyretic activity (*p* < 0.01) with all the doses used when compared to the control group [15].

### **6.10 Anthelmintic activity**

Helminthiasis is one of the most popular disease and one of the most serious diseases [56]. Stem powder of *A. aspera* extracted with methanol and water. It was extracted by using maceration process. Its chemical constituents such as saponins A, saponins B, terpenoids and volatile oils showed anthelmintic activity. Albendazole used as a standard drug for the comparison of anthelmintic activity of *A. aspera.* Extract of it's used at different concentration (2.5, 5, 10 and 20 mg/ml) for testing anthelmintic activity which based on the determination of time of paralysis and time of death of the worms. All the methanolic extracts of *A. aspera* showed the better protection against the helminthiasis [57].