Stat-Ease Blog

Here's the latest Publication Roundup! In these monthly posts, we'll feature recent papers that cited Design-Expert® or Stat-Ease® 360 software. Please submit your paper to us if you haven't seen it featured yet!

Featured Articles

Microwave-assisted extraction of bioactive compounds from Urtica dioica using solvent-based process optimization and characterization
Scientific Reports volume 15, Article number: 25375 (2025)
Authors: Anjali Sahal, Afzal Hussain, Ritesh Mishra, Sakshi Pandey, Ankita Dobhal, Waseem Ahmad, Vinod Kumar, Umesh Chandra Lohani, Sanjay Kumar

Mark's comments: Kudos to this team for deploying a Box-Behnken response-surface-method design--convenient by only requiring 3 levels of each of their 3 factors (power, time and sample-to-solvent ratio)--to optimize their process. Given all the raw data I was able to easily copy it out and import it into my Stat-Ease software and check into the modeling--no major issues uncovered. The authors did well by diagnosing residuals and making use of our numerical optimization tools to find the most desirable factor combination for their multiple-response goals.

Be sure to check out this important study, and the other research listed below!

More new publications from July

1. Improving the heterotrophic media of three Chlorella vulgaris mutants toward optimal color, biomass and protein productivity
   Scientific Reports volume 15, Article number: 23325 (2025)
   Authors: Mafalda Trovão, Miguel Cunha, Gonçalo Espírito Santo, Humberto Pedroso, Ana Reis, Ana Barros, Nádia Correia, Lisa Schüler, Monya Costa, Sara Ferreira, Helena Cardoso, Márcia Ventura, João Varela, Joana Silva, Filomena Freitas, Hugo Pereira
2. Calibration and establishment for the discrete element simulation parameters of pepper stem during harvest period
   Scientific Reports volume 15, Article number: 21143 (2025)
   Authors: Jiaxuan Yang, Jin Lei, Xinyan Qin, Zhi Wang, Jianglong Zhang, Lijian Lu
3. Design Expert Software Being Used to Explore the Factors Affecting the “Water Garden”
   American Journal of Analytical Chemistry, 16, 107-116
   Authors: Zelin Miu, Yichen Lu
4. Quality improvement of recycled carbon black from waste tire pyrolysis for replacing carbon black N330
   Scientific Reports volume 15, Article number: 23726 (2025)
   Authors: Tawan Laithong, Tarinee Nampitch, Peerapon Ourapeepon, Natacha Phetyim
5. Development and in vitro characterization of embelin bilosomes for enhanced oral bioavailability
   Journal of Research in Pharmacy, Year 2025, Volume: 29 Issue: 4, 1616 - 1626, 05.07.2025
   Authors: Shreya Firake Devanshi Pethani Jeet Patil Avinash Bhujbal Rahul Gondake Dhanashree Sanap , Sneha Agrawal
6. Performance optimization and mechanism research of C20 coal gangue concrete based on response surface and water resistance
   AIP Advances, 15, 075316 (2025)
   Authors: Yong Cui, Xiwen Yin, Qiuge Yu
7. Multi-objective optimization of boiler combustion efficiency and emissions using genetic algorithm and recurrent neural network in 660-MW coal-fired power plant
   Eastern-European Journal of Enterprise Technologies, 3(8 (135), 23–33
   Authors: Mohamad Arwan Efendy, Ahmad Syihan Auzani, Sholahudin Sholahudin
8. Optimization and characterization of polyhydroxybutyrate produced by Vreelandella piezotolerans using orange peel waste
   Scientific Reports volume 15, Article number: 25873 (2025)
   Authors: Mahmoud H. Hendy, Amr M. Shehabeldine, Amr H. Hashem, Ahmed F. El-Sayed, Hussein H El-Sheikh
9. Multi-functional electrodialysis process to treat hyper-saline reverse osmosis brine: producing high value-added HCl, NaOH and energy consumption calculation
   Environmental Sciences Europe volume 37, Article number: 121 (2025)
   Authors: Haia M. Elsayd, Gamal K. Hassan, Ahmed A. Affy, M. Hanafy, Tamer S. Ahmed
10. Quality by Design-Based Method for Simultaneous Determination of Glimepiride and Lovastatin in Self-Nano Emulsifying Drug Delivery System
    Separation Science Plus, 8: e70097
    Authors: Priyanka Paul, Raj Kamal, Thakur Gurjeet Singh, Ankit Awasthi, Rohit Bhatia
11. Sustainable Adsorbents for Wastewater Treatment: Template-Free Mesoporous Silica from Coal Fly Ash
    Chemical Engineering & Technology, 48: e70077
    Authors: Thapelo Manyepedza, Emmanuel Gaolefufa, Gaone Koodirile, Dr. Isaac N. Beas, Dr. Joshua Gorimbo, Bakang Modukanele, Dr. Moses T. Kabomo

Here's the latest Publication Roundup! In these monthly posts, we'll feature recent papers that cited Design-Expert® or Stat-Ease® 360 software. Please submit your paper to us if you haven't seen it featured yet!

Featured Article

Enhanced 4-chlorophenol adsorption from aqueous solution using eco-friendly nanocomposite
Ecological Engineering & Environmental Technology, 26(5), pp.174-189
Authors: Fadia A. Sulaiman, Rasha Khalid Sabri Mhemid, Noor A. Mohammed

Mark's comments: It is great to see the application of response surface methods (RSM) to reduce the release of toxic chlorophenols to our environment, particularly via such an eco-friendly process utilizing a natural polymer--xanthan gum.  The 3D graphics are compelling and well supported by the reported statistics.  I also appreciate that all the raw data is including, making it possible for me to reproduce the results.

Be sure to check out this important study, and the other research listed below!

More new publications from May

1. Design of Experiments Assisted Formulation Optimization and Evaluation of Efavirenz Solid Dispersion Adsorbate for Improvement in Dissolution and Flow Properties
   Drug Design, Development and Therapy, 2025;19:3715-3734
   Authors: Mujtaba MA, Rashid MA, Alhamhoom Y, Gangane P, Jagtap MJ, Akbar MJ, Wathore SA, Kaleem M, Elhassan GO, Khalid M
   
2. Design of Experiments Approach for the Development of a Validated UPLC-Q-ToF/MS Method to Quantitate Soy-Derived Bioactive Peptide Lunasin in Rabbit Plasma: Application to a Pharmacokinetic Study
   Biomedical Chromatography, 2025, 39: e70098
   Authors: Kowmudi, G., Anoop, K., Varshini, M., Nagappan, K., Konanki, S., Praveen, T
   
3. Development of a Stability-Indicating RP-HPLC Method for Pioglitazone in Cubosomal and Biological Matrices: A Quality by Design-Driven, Lean Six Sigma, and Green Chemistry Approach
   Separation Science Plus, 2025, 8: e70055
   Authors: Vaibhavi D. Torgal, Vinayak Mastiholimath, Rahul Koli
   
4. Optimization process of coffee pulp wines combined with the artificial neural network and response surface methodology
   Scientific Reports, volume 15, Article number: 16684 (2025)
   Authors: Rongsuo Hu, Fei Xu, Liyan Zhao, Wenjiang Dong
   
5. Formulation optimization of furosemide floating-bioadhesive matrix tablets using waste-derived Citrus aurantifolia peel pectin as a polymer
   Scientific Reports, volume 15, Article number: 16704 (2025)
   Authors: Ebrahim Abdela Siraj, Yohannes Mulualem, Fantahun Molla, Ashagrachew Tewabe Yayehrad, Anteneh Belete
   
6. Bio-induced overproduction of heterocycloanthracin-like bacteriocin in Lysinibacillus macroides by Aspergillus austroafricanus: optimization of medium conditions and evaluation of potential applications
   BMC Biotechnology volume 25, Article number: 41 (2025)
   Authors: Philomena Edet, Maurice Ekpenyong, Atim Asitok, David Ubi, Cecilia Echa, Uwamere Edeghor, Sylvester Antai
   
7. Structural characterization, gelling properties, and beef preservation applications of pectin extracted from sweetpotato residue using a hydrothermal method
   International Journal of Biological Macromolecules, Volume 314, 2025, 144348
   Authors: Linchong Hui, Chan Zhang, Junjie Yu, Man Liu, Kunlong Yang, Ling Shen, Bingqian Hu, Jun Tian, Yong-xin Li
   
8. Calibration of soil contact parameters for planting sand shrubs in the desert regions of Inner Mongolia
   Scientific Reports volume 15, Article number: 17231 (2025)
   Authors: Zhang Nannan, Pei Chenghui, Zhang Yantang, Cui Shaoyu, Liang Lingzhi, Liu Zhigang
   
9. Development of a novel tailor-made cocktail from recombinant crude enzymes for efficient saccharification of pretreated elephant grass
   International Journal of Biological Macromolecules, 26 May 2025, 144645
   Authors: Aishwarya Aishwarya, Arun Goyal
   
10. Sustainable discharge printing of marigold-dyed cotton with eucalyptus wood ash extract and its optimisation by response surface methodology
    Coloration Technology, first published: 27 May 2025
    Authors: Harshal Patil, Devansh Chaudhari, Ashok Athalye
    
11. Chitosan-coated nanostructured lipid carriers of amantadine for nose-to-brain delivery: formulation optimization, in vitro-ex vivo characterization, and in vivo anti-parkinsonism assessment
    International Journal of Biological Macromolecules, Volume 316, Part 2, June 2025, 144497
    Authors: Archita Kapoor, Abdul Hafeez, Poonam Kushwaha, Nargis Ara

Next in our 40th anniversary “Ask an Expert” blog series is John "Jay" Davies, who's an absolute rock star when it comes to teaching and implementing DOE. He's lent us his expertise before - see this talk from our 2022 Online DOE Summit - and he shared an anecdote with our statistical experts about how he approaches switching to DOE methods when working with new groups in the Army. He kindly agreed to let us publish it as part of this series.

For the past 14 years, I’ve been a Research Physicist with the U.S. Army DEVCOM Chemical Biological Center at Aberdeen Proving Grounds, MD as a member of the Decontamination Sciences Branch, which specializes in developing techniques/chemistries to neutralize chemical warfare agents. I’m dedicated to applied statistical analysis ranging from multi-laboratory precision studies to design of experiments (DOE). The Decontamination Sciences Branch has been integrating DOE methods into many of their chemical agent decontamination research programs.

I’m happy to report that the DOE methods here at the Chemical Biological Center are really catching on. I collaborated on 24 DOEs from 2014 through 2021. Then, in 2021-22, we completed 26 DOEs across 10 different programs. I’ve been doing a lot of mixture-process DOEs with the Bio Sciences groups for synthetic biology and bio manufacturing applications, and once the other groups saw the information that we were getting from just a single day’s worth of data, they too wanted to try DOE.

Lately, I’ve changed the formula that I use for the initial consultations when visiting a group that has expressed an interest in DOE but has never used DOE before. Previously we’d go right into their project, and I’d tell them how we might construct a DOE for their application. However, I’ve found that it’s too much of a culture shock if we go right into talking about what a DOE for their application might look like. Instead, especially if I’m working with a group that has no DOE experience at all, I now devote about 1 hour to discuss DOE methods in general before we even mention their actual application. In this discussion, I reveal the major differences that they are going to see with a DOE, which are:

• We’re not going to replicate every sample, we may even have zero exact replication.
• We’re not going to test every possible combination of factors.
• Sample sizes are going to be 70% to 95% smaller than what they are used to doing.
• We are not going to change “one factor at a time”, in fact we’ll be changing all factors at once.
• The designs might look chaotic, but they are strategically created to contain a hidden orthogonal structure, along with hidden replication, that is not apparent.
• You will see that many of the DOE samples contain factor combinations that don’t seem to make sense. This is because each sample is not designed as a stand-alone shot at optimization. Rather, the samples cumulatively are working in concert to tease out the influences of each factor. This will let us fit a predictive model that we will then use to predict the optimal settings of factors.

Recently, I was following this format with a group that had never used DOE before. We had a great back-and-forth dialog as I went through the bullets above and explained a bit about each point. They asked many questions and were really following along. Then, after about an hour we got into their application and I just sketched out a prototype quadratic mixture-process DOE that I thought would give them a good idea of what the initial DOE might look like, with 30 samples in total. I then went over what some simulated outputs for the DOE generated prediction model might look like. At this point one of them stopped me and with a very perplexed look on his face, said “hold on, hold on…wait a minute here. Are you telling us that if we run just those 30 samples, we would be able to predict the optimal formulation and the optimal process setting for this system?“

This scientist had been following along, asking questions and really absorbing the information in the past hour as we walked through the DOE basics, but I could see that at that moment things were just sinking in. He realized the ramifications of what we had just discussed – typically, this group might have had to run several hundreds of samples to characterize similar systems, but with DOE they would only need about 30 samples. I responded to his question saying, “Yes that’s exactly what I’m telling you. We’ve run dozens of these mixture-process DOEs, many of them much more complex than this system, and they do work.” This individual, a mid-career researcher, then responded, “How is it possible that we have not heard of this stuff before?” I told him, “I can’t give you a good answer to that one.”

And there you have it! Let us know if you want to talk about saving time & money with DOE: our statistical experts and first-in-class software make it easier than ever.

DISTRIBUTION STATEMENT A. Approved for Public Release: Distribution is Unlimited

Welcome to the first entry in our 40th anniversary Ask An Expert series, where we talk to current and past power users of Design-Expert® and Stat-Ease® 360 software about their experience with design of experiments (DOE) and our software. For this post, we interviewed Shari Kraber, formerly the Client Success Manager, Workshop Manager, and Senior Instructor for Stat-Ease. Shari retired in 2022 after nearly 3 decades of helping clients across all industries learn DOE and implement it to save time & money making breakthrough improvements on their products & processes.

What’s the biggest benefit to educating your team about DOE?

You’ll break the habit of testing changes one at a time. Many systems will have unknown interactions, and only structured DOE test plans will reveal them. Your team will learn a new way of approaching problems, which helps the company in the long run.

You spent so many years helping folks change from one-factor-at-a-time testing to using DOE. What’s something about DOE that more people should know?

Remember that DOE is about trying to get a bunch of information from a small sample of a large process. The analysis does not need to be perfect in order to be useful. Don’t get paralysis by analysis – just find a simple and reasonable model and then CONFIRM the results. You should use software to design and plan the experiments – a good (robust) design will help offset the inevitable problems encountered while running the physical experiment, so that the analysis will be useful enough to make business decisions.

Your background is as a process engineer at 3M, but you always insisted that anyone working with formulations should use mixture designs. Why?

When the response is dependent on the proportions of the ingredients, then two things make this different from a process design:

1. The design space changes to accommodate the proportions of the ingredients.
2. The polynomial model is a different form (use a Scheffé polynomial) so that the nonlinear components of the system are modeled correctly. A traditional polynomial will NOT model this system correctly!!

So, what’s the best way to train your team on DOE?

I think distance learning is great. Stat-Ease started doing distance-learning training via Zoom during the COVID-19 pandemic, and it remains a popular choice for teams. The big advantage of distance learning is that the massive amount of information provided is more digestible in half-day segments. The in-person training is pretty intense and is not as ideal educationally. Yes, it is nice to have a live trainer, but honestly the retention of the materials is BETTER using distance learning.

Finally, what features of Stat-Ease software do you want more folks to know about?

I have quite a few recommendations!

First, there are several great editing features if you right-click on any graphs in the software:

• Edit axes
• Plant flags (then right-click on them to edit info)
• Graph Preferences to edit font sizes, colors, display options

And some more:

1. Confirmation node – add the settings for the confirmation runs that you want to do and it provides the 95% interval that should contain the mean of those runs.
2. You can change the layout options when you have multiple tabs or windows in your analysis. The green + allows you to add more graphs to the display, such as viewing 2 interaction graphs at once, or a contour and 3D plot at the same time.
3. In the Analysis Summary, the Coefficients Table can be Transposed by right-clicking on the top left corner square. (Transpose is now available on all tables, but particularly helpful on this one.)

If you’re ready to train your team on DOE, check out our public training options or email us with your questions. Shari still teaches classes on a part-time basis, and our whole team would love to get you rolling with best practices for DOE.

Thanksgiving is fast approaching—time to begin the meal planning. With this in mind, the NBC Today show’s October 22nd tips for "75 Thanksgiving desserts for the sweetest end to your feast" caught my eye, in particular the Donut Loaf pound cake. My 11 grandkids would love this “giant powdered sugar donut” (and their Poppa, too!).

I became a big fan of pound cake in the early 1990s while teaching DOE to food scientists at Sara Lee Corporation. Their ready-made pound cakes really hit the spot. However, it is hard to beat starting from scratch and baking your own pound cake. The recipe goes backs hundreds of years to a time when many people could not read, thus it simply called for a pound each of flour, butter, sugar and eggs. Not having a strong interest in baking and wanting to minimize ingredients and complexity (other than adding milk for moisture and baking powder for tenderness), I made this formulation my starting point for a mixture DOE, using the Sara Lee classic pound cake as the standard for comparison.

As I always advise Stat-Ease clients, before designing an experiment, begin with the first principles. I took advantage of my work with Sara Lee to gain insights on the food science of pound cake. Then I checked out Rose Levy Beranbaum’s The Cake Bible from my local library. I was a bit dismayed to learn from this research that the experts recommended cake flour, which costs about four times more than the all-purpose (AP) variety. Having worked in a flour mill during my time at General Mills as a process engineer, I was skeptical. Therefore, I developed a way to ‘have my cake and eat it too’: via a multicomponent constraint (MCC), my experiment design incorporated both varieties of flour. Figure 1 shows how to enter this in Stat-Ease software.

By the way, as you can see in the screen shot, I scaled back the total weight of each experimental cake to 1 pound (16 ounces by weight), keeping each of the four ingredients in a specified range with the MCC preventing the combined amount of flour from going out of bounds.

The trace plot shown in Figure 2 provides the ingredient directions for a pound cake that pleases kids (based on tastes of my young family of 5 at the time) are straight-forward: more sugar, less eggs and go with the cheap AP flour (its track not appreciably different than the cake flour.)

For all the details on my pound cake experiment, refer to "Mixing it up with Computer-Aided Design"—the manuscript for a publication by Today's Chemist at Work in their November 1997 issue. This DOE is also featured in “MCCs Made as Easy as Making a Pound Cake” in Chapter 6 of Formulation Simplified: Finding the Sweet Spot through Design and Analysis of Experiments with Mixtures.

The only thing I would do different nowadays is pour a lot of powdered sugar over the top a la the Today show recipe. One thing that I will not do, despite it being so popular during the Halloween/Thanksgiving season, is add pumpkin spice. But go ahead if you like—do your own thing while experimenting on pound cake for your family’s feast. Happy holidays! Enjoy!

To learn more about MCCs and master DOE for food, chemical, pharmaceutical, cosmetic or any other recipe improvement projects, enroll in a Stat-Ease “Mixture Design for Optimal Formulations” public workshop or arrange for a private presentation to your R&D team.