Meet the editors

Paulo Pereira received his PhD (Biotechnology, specialization Microbiology) from the Catholic University of Portugal. Dr. Pereira is the coordinator of the R&D Department, Portuguese Institute of Blood and Transplantation, Lisbon. He has been recruited as a quality and laboratory expert for seminars and professional laboratory meetings throughout Europe. He has 22+ years of experience as a senior researcher and quality manager,

having held key scientific leadership roles and successfully led quality teams. His areas of specialization are quality control of medical laboratory tests (selection, verification, validation, internal quality control, external quality assessment, measurement uncertainty, and reference intervals); successful practice of ISO and CLSI protocols; sampling methodologies and statistical quality control; risk management; GLP in medical laboratories; and GMP in blood banks.

Sandra Xavier received her PhD in Nursing from the University of Lisbon, Portugal. Dr. Xavier is a professor in the School of Health, Polytechnic Institute of Beja, Portugal, and an integrated researcher in NURSE'IN—Nursing Research Unit for South and Islands, Portugal. She has 22+ years of professional experience in nursing and 12+ years of professional teaching experience. She is also a recognized speaker at various national and international

meetings. Dr. Xavier is a member of several commissions. Her areas of specialization are quality control in nursing and blood banks; nursing in palliative care, community nursing, and public health; emotional labor of nursing practice in community health; emotions in health and emotional education; and emotional competencies in nursing.

**Preface III**

Quality Management **1**

**Chapter 1 3**

**Chapter 2 21**

Quality Control **39**

**Chapter 3 41**

**Chapter 4 55**

**Chapter 5 77**

**Chapter 6 95**

**Chapter 7 115**

Determination of Essential Parameters for Quality Control in Fabrication of

*by Matej Možek, Borut Pečar, Drago Resnik and Danilo Vrtačnik*

Determination of Impurities in Pharmaceuticals: Why and How?

Quality Management Practices in Indian SMEs

Total Quality Management in a Resource-Starved Nation

*by Martins Emeje, Kokonne Ekere, Olubunmi Olayemi, Christianah Isimi* 

Analytical Method Validation as the First Step in Drug Quality Control

Quality in Testing Laboratories: A Real Case in a Spanish Fuel Laboratory *by Mª Mercedes del Coro Fernández-Feal, Luis R. Sánchez-Fernández* 

**Section 1**

**Section 2**

*by Ayon Chakraborty*

Contents

*and Karniyus Gamaniel*

Sample Traceability in Toxicology

*and Blanca Sánchez-Fernández*

Piezoelectric Micropumps

*by Kung-Tien Liu and Chien-Hsin Chen*

*by Laura Börgel Aguilera and Melissa Schulthess*

*by Sigrid Mennickent and Marta de Diego*

## Contents


Preface

Quality management (QM) ensures the reliability and consistency of products or services in a sustainable organization. Consistent quality of products or services is the target of any manufacturer to satisfy the interested parties. "Quality" is defined by ISO 9000:2015 as the "degree to which a set of inherent characteristics of an object fulfills requirements." Typically, QM is composed of four main components: quality planning, quality assurance, quality control (QC), and quality improvement. ISO interprets QC as "a part of quality management focused on fulfilling quality requirements," and it is a process by which entities review the quality of all factors involved in production. Therefore, QM effectiveness is dependent on QC

QM practices are the basis for the successful implementation and maintenance of any QM system. They should assure the compliance of the seven ISO 9000:2015 principles: customer focus, leadership, engagement of people, process approach, improvement, evidence-based decision-making, and relationship management. In addition, total quality management (TQM) is a strategy that requires not only a QM cycle but also the satisfaction of other basics such as technical requirements. "Total" is understood as the full involvement of all parts of the organization in the continuous satisfaction of interested parties. Compared to a QM strategy based merely on the plan-do-check-act cycle, as referred to in ISO 9001:2015, TQM requires a more detailed QC policy. ISO/IEC 17025:2017 is an example of a global standard based on a TQM policy for the standardization and accreditation of the competence of testing and calibration laboratories. The application of management practices or TQM

represents a challenge principally in emerging and developing economies.

based on the observation of the entire production or on sampling.

This book is focused on new trends and developments in QM and QC in several types of industries from a worldwide perspective. Its content has been organized into two sections and seven chapters written by well-recognized researchers worldwide. The QM section includes a viewpoint of TQM in Nigeria and a discussion of

QC strategy and practices vary according to the type of organization. At the debut stage, the essential parameters for QC are defined. They vary according to the manufacturing or analytical process. Sampling and traceability types are part of these. Sample traceability is critical to assuring that the reported results are consistent with the sample. A corrective-action/preventive-action (CAPA) viewpoint shows that most of the adverse events reported in laboratories are in the pre-examination phase. This phase is closely related to the source of nonconforming sample traceability. Therefore, a control policy is critical to the reliability of the reports. At the validation stage, the use of experimental lab data distinguishes analytical method validation from the previous selection and verification steps. It is the primary stage using mainly intra-laboratory data. Method validation using appropriate statistical tools should verify the uncertainty of the results in conditions that cannot be entirely controlled by an internal QC design. Obviously, QC cannot be debated just in a testing and calibration laboratory perspective. Control during the manufacturing stage is generally associated with a statistical process control methodology

strategy.
